Research Questions

Exercise 1: What are the spatial patterns of vegetation species distribution across sampled points?

Exercise 2: Is there spatial cross-correlation between pairs of plant species presence along a sampled transect? Is there spatial cross-correlation between the presence of a given plant species and elevation along sampled transects?

Exercise 3: How did vegetation presence change between 2015 and 2019? Where was there a gain or loss of vegetation, and what areas remained vegetated or unvegetated?

Datasets

For the first component of Exercise 1, I examined a 2021 elevation and vegetation dataset. Elevation was collected with an RTK-GPS and vegetation species presence and maximum height were recorded at each point. Sampling was done every 50m on a grid. For the second component (autocorrelation) of Exercise 1, and for Exercise 2, I used an elevation and vegetation dataset from 2021 resampling of permanent transects (which have been sampled nearly annually since 2009). The same sampling method was used as the previously described dataset, except sampling was done every 1m along each 50m transect. Additional data was collected but was not used for these analyses.

For Exercise 3, I used 4-band multispectral aerial imagery from with 0.25m resolution from 2015 and 2019.

Hypotheses

I predicted that broadly, the spatial pattern of plant species would be clustered at the site scale, due to differences in abiotic conditions (i.e. salinity and elevation). I predicted that there would be some differences between spatial patterns, which I expected is due to smaller scale differences in abiotic conditions as well as biological interactions (not being investigated). In terms of site-wide vegetation change, I predicted there would be a net gain in the extent of emergent high marsh over time, if with the restoration of tidal influence there has been sufficient sediment availability for vertical accretion to occur via a positive feedback loop between accretion and vegetation growth (Kirwan et al. 2013). (I did not end up looking at specific habitat types or plant communities, but still would predict vegetation gain on the mudflat in higher elevation areas.)

Approaches

Exercise 1: point pattern analysis (average nearest neighbor) and autocorrelation

Exercise 2: cross-correlation

Exercise 3: confusion matrix and change detection map

Results

In Exercise 1, I produced maps displaying the presence and absence of two marsh plant species, marsh jaumea and saltgrass. I produced statistical relationships for the average nearest neighbor analysis. For this analysis, I selected a subset of points so that there would not be gaps in the sampling coverage; the sites sampled were not contiguous, creating gaps that would have affected results. The observed mean distance for saltgrass is 48.61m, which reflects the 50m sampling grid. The observed mean distance for marsh jaumea is 70.14m.

For the autocorrelation component, I produced plots. For one of the transects, I found significant autocorrelation for lags 1-4. I then appended two transects from a different restoration unit to increase the sample size to 100 points, and found significant autocorrelation at all lags, decreasing over space.

For Exercise 2, I produced cross-correlation plots for the relationship between saltgrass and jaumea presence/absence along two transects and between saltgrass presence/absence and elevation. For the pairs of species, there was no significant cross-correlation on one of the transects. For the other transect, there is some significant cross-correlation (maximum ~0.35) for lags -4 to -13, decreasing over space. I believe this indicates significant cross-correlation of saltgrass to the left of a point with jaumea. For elevation and saltgrass, there is significant cross-correlation between lags -2 to 2, and the plot is fairly symmetric. For the other transect, there is significant cross-correlation from lags -4 to 6. These findings make sense to me, as I tended to see saltgrass at higher elevations within the tidal frame.

For Exercise 3, I produced a map of vegetation change, and a sort of confusion matrix (summary statistics for the percent represented by each category). I believe that there are inaccuracies in this analysis (see next section), but it does appear that by 2019 there was some vegetation colonization surrounding vegetation that existed in 2015.

Analysis Learnings

In Exercise 1, the average nearest neighbor analysis didn’t turn out to work well for my data, due to gaps between sampled areas, as well as observer-determined sampling points (50m grid). For the autocorrelation analysis I used the transect data because pulling out a transect from the grid wasn’t enough data to use. I learned that the presence (or absence) of saltgrass at one point did tell me something about the presence (or absence) at the next few points for one transect, and when I appended transects for another unit, there was autocorrelation at every point.

In Exercise 2, I used a very limited set of data (one pair of species for two transects, and one species and elevation for two transects). The plots look relatively different for the sets of transects, which I expected as I consciously chose two that would be different (different habitat, one with a transition from mudflat to vegetation, etc.). There was significant cross-correlation between saltgrass and elevation, and I’m interested in investigating relationships for more species and transects. Additionally, I think using salinity instead of elevation will be informative.

Exercise 3 was primarily useful for learning the process of a basic change detection analysis and becoming aware of data issues I’ll need to address. For example, some parallel patterns of vegetation gain and loss on the northeastern island indicated that the channels are not lining up well and I will likely need to do new georeferencing. Additionally, I believe there are areas of the large mudflat that were categorized as vegetated but are actually algae, showing up as a loss of vegetation.

Significance

The results from autocorrelation and cross-correlation in Exercises 1 and 2 show some promise for predicting the presence of vegetation. If this is the case upon further investigation, this may be useful in modeling future post-restoration trajectories (i.e. under different sediment accretion scenarios, where would vegetation be predicted to colonize?). Additionally, cross-correlation between species may provide information on common plant associations. I think that these analyses are likely most useful as initial steps that might inform future analyses.

The “confusion matrix” and map of change detection give a site-wide view of vegetation change (though currently without the nuance of habitat type). Once some issues are addressed and new results are produced, the map will be a helpful visual of patterns of change over time. For example, a freshwater wetland transitioned to mudflat post-levee breach in 2009, and there need to be elevation gain for vegetation colonization to be possible. Maps such as this produce a visual of whether this has occurred, whether it’s occurring in specific areas and/or as a result of known or unknown processes (i.e. the eastern side where it was predicted there would be more sediment input). This analysis contributes to monitoring efforts, and can inform resource managers with adaptive management decision-making (I.e. could plantings or sediment application be warranted)? This research fits into a larger monitoring framework at the site. Additionally, monitoring contributes to knowledge about the time frame and trajectory of restoration, which may inform the design of future restoration projects.

Software learning

I used ArcGIS Pro for data visualization, point pattern analysis, and the confusion matrix and change map. The steps involved in these analyses introduced me to more geoprocessing/spatial analysis tools in Arc. I used R for data manipulation (into the presence/absence and elevation format I needed) and for spatial autocorrelation and cross-correlation, which were new R functions for me. I did not end up using Python or Modelbuilder in Arc.

Statistics learning

I learned that point pattern analysis is a good option for presence absence data, though my sampling points being observer-determined hindered the average nearest neighbor analysis (as well as gaps between sampling sites). I chose spatial autocorrelation because I have evenly spaced count data, and determined that presence/absence data would indeed work as a count. In the limited bit I’ve heard about autocorrelation in the past, it’s been in terms of checking for violations of model assumptions (I.e. regression analyses), so it was helpful to learn about new applications for univariate analysis.

Cross-correlation was a new statistical method to me. One limitation I found was that I was unable to run this analysis in the instance where a species was present at every sampling point. In working on change detection, I was able to think through ways to deal with the issue of NDVI not being standardized between years, and using unsupervised classification. I hadn’t run these analyses before, so I learned a lot about the process!

Evolving question

My objective in “My Spatial Problem” was to explore the spatial patterns of vegetation species and communities, how vegetation community structure has changed since restoration, and how this related to geomorphological change via changes in sediment delivery and inundation regimes.

Wow, that was a broad question! For the first two exercises, I ended up focusing on individual vegetation species, rather than tackling any sort of community analysis (to come in future research questions). My original question was missing relating vegetation species presence to a variable B (elevation for exercise 2); I had skipped ahead to broadly stating that I wanted to relate vegetative change to geomorphic change.

My restated questions are: How is species distribution related in space to physical environmental variables (i.e. elevation, salinity, proximity to channels)? How are patterns of vegetation change related to geomorphic change? 

Future techniques

I would like to continue working on change detection, looking more into habitat classification methods. For example, I would like to learn more about unclassified habitat supervision and whether manual adjustments need to be made for areas of potential misclassification (such as algae being classified as veg). In the future, I will incorporate empirical data to classify points on spectral signatures, and then do image classification. Additionally, I’d like to look into other values for classification such as the Soil Adjusted Vegetation Index (SAVI) that adjust for soil reflectance, or other classification methods such as object-based classification. Ultimately, I will want to perform change detection by habitat type (mudflat, salt marsh, riparian floodplain, etc.) to better quantify restoration progress.

I would like to explore dissimilarity analyses, such as Bray Curtis, to look at changes over time in resampled vegetation quadrats. I’ll be exploring ordination techniques once I’m further along in thinking about vegetation community analysis. I’ll also be doing a lidar change detection analysis and relating this to vegetation change (technique options to be explored!).

J.A. Laney – Final Project Blog Post GEOG 566

Background & Research Questions

One chapter of my dissertation research is focused on exploring patterns of functional diversity and heterogeneity–diversity relationships in communities of disparate small-bodied vertebrate taxa. I am interested in (1) understanding how functional diversity (estimated by metrics such as functional richness, functional divergence, functional redundancy, functional dispersion, etc.) vary within and across communities of songbirds and small mammals along the elevational gradient of my study system, and (2) relating these patterns to changes in habitat heterogeneity that occur across elevation. In this course I focussed on the first of these two objectives by exploring autocorrelation of functional metrics of communities and also by describing the dissimilarity of communities by distance. 

In Exercise 1, I set out to assess the spatial autocorrelation of various functional metrics across the 16 localities for which I have both bird and mammal survey data. In Exercise 2, I asked how taxonomic and functional Sørensen dissimilarity of passerine bird and small mammal communities in my dataset vary as a function of geographic distance. In Exercise 3., I explored the sensitive of my analysis dissimilarity of bird-mammal communities to changes in the underlying data, specifically when filtering bird observations by distance from observation points?

Description of the Dataset

The dataset I analyzed is comprised of records of small mammal and bird occurrences, as well as associated habitat information, from a comprehensive biological survey project I am leading on a desert-montane gradient in the Northern Great Basin: The Steens Mountain Resurvey Project. To date, I have surveyed 21 discrete localities across the elevational gradient of this region with sites ranging in elevation from the mountain’s summit to the basin floor (Figure 1.). Each locality consists of a circular survey area with an approximate area of 0.8km. Localities were selected based on the availability of historical small mammal survey data and to maximize sampling of the elevations and habitats in the Steens Mountain-Alvord Desert system. Of the total localities in this project, I selected 16 sites where both bird and small mammal data were collected for the years 2019 and 2021. 

Within each survey locality (Figure 1.), I conducted avian point count surveys for breeding songbirds and small mammal trapline surveys in the summers of 2019 and 2021. Point-counts were conducted within 4 hours of sunrise between early June and early July to coincide with hours of peak bird activity during the breeding season in this region. I detected birds by sight or sound and recorded each bird’s distance from the observer to the nearest meter using a digital rangefinder. Small mammal trapline surveys consist of removal trapping along multiple traplines (usually 3-6) arrayed at each survey locality to capture the heterogeneity of habitat types and plant communities and to detect the highest diversity in the shortest amount of time. Using a combination of baited Sherman live-traps, Havahart traps, museum snap traps, Victor rat traps, and pitfall traps along traplines, I targeted rodent and shrew species (Orders Rodentia and Eulipotypla) (<500g). In most scenarios, mammal surveys were conducted for a minimum of four nights to maximize sampling. Small mammal surveys are conducted between early July and mid-August, tailored to align with periods of historical sampling while accounting for moon phase.  

Analytical Approach

Methods – Exercise 1.

To characterize functional diversity of the passerine-rodent-shrew communities, I calculated functional metrics, such as functional richness, functional redundancy, functional divergence, and functional dispersion of communities using unique trait combinations of the birds and mammals detected in each survey locality in this project. These metrics were derived using the “mFD” packing in program r for each locality using the abundance data of the birds and mammals detected during field surveys. To assess spatial autocorrelation of the various functional metrics across the 16 localities for which I had both bird and mammal survey data. To do so, I calculated the global Moran’s I correlation coefficient for each functional metric. 

I began by sorting my bird point-count records by taxonomic order (Passeriformes) and distance (< 100 meter from observer). I then joined bird data with small mammal data by locality. I selected unique mensural traits from the ecological literature that were both biologically relevant and shared both the small-bodied songbirds and small mammals in this system. These traits included body mass, litter/clutch size, % diet invertebrate, % diet vertebrate, %diet scavenged, % diet seed, % diet fruit, and % diet plant other. Traits came from two different databases, the “Amniote” database, and “Elton” database.

I calculated standard functional metrics for each locality using the “mFD” r package and matrices of species by site, and traits by species. To calculate functional redundancy, I first binned traits using the Sturgis algorithm to derive “functional entities, or species with unique trait combinations (UTCs). In r, I  generated a distance matrix of localities using their associated geographic coordinate information. I then took the inverse of the matrix values and replace the diagonal entries with zero to complete a distance matrix that I could use to assess spatial autocorrelation. Once I had functional metrics for each locality, I computed Moran’s I in the r programming language using the ‘Moran.I’ function in the ‘Ape’ package. This was a relatively straight forward process using minimal lines of code, though I fist had to create a custom function to compute the coefficient across multiple columns representing individual functional metrics.

Methods – Exercise 2.

For this exercise 2, I was interested in exploring how the taxonomic and functional dissimilarity of the passerine bird and small mammal communities in my dataset vary as a function of geographic distance. Specifically, I modeled the Sørensen dissimilarity between localities, as well as its turnover and nestedness components, as both a function of geographic distance in kilometers and xyz distance (latitude, longitude, and elevation) to see how dissimilarity is related to distance.

I began by calculating the pairwise taxonomic dissimilarity of all pairs of localities using the Sørensen index in the ‘betapart’ package in R. The output of this package provides overall Sørensen distance, as well as the turnover and nestedness components of the dissimilarity.  I then calculated the pairwise functional dissimilarity of all pairs of localities using the functional beta diversity function in the ‘mFD’ package (which utilizes the ’betapart’ framework). The output provides the functional Sørensen distance between all pairs of localities, as well as the functional turnover and nestedness components of the dissimilarity. Next, using the ‘sf’ package, I created geographic centroids for all my localities and calculated the pairwise geographic distance in km for all localities using the ‘st_distance’ function in that same package. Considering that these localities are distributed along an elevation gradient in a mountain-desert system,  elevation is “built into” the geographic distances between localities. However, I also wanted to explicitly incorporate elevation into the distances. Thus, I used the ‘scatterplot3d’ package to create a 3-dimensional space composed of the xy (latitude and longitude) and z (elevation in meters) coordinates of the localities (Figure 2.). I then used the ‘dist’ function to calculate the xyz distance between all pairs of localities in this space. Finally, I modeled distance decay of taxonomic and functional pairwise dissimilarity, turnover, and nestedness for all pairs of localities against geographic distance in xy space and xyz space and plotted the results. These analyses were done using the ‘decay.model’ and ‘plot.decay’ functions in the ‘betapart’ package. 

Methods – Exercise 3.

For this exercise 3., I was interested in assessing parameter sensitivity of the spatial pattern I described in Exercise 2. I tested the sensitivity of the dissimilarity distance decay analyses by filtering the dataset so that it only contained passerine bird observation that were within 50 meters or less of the points during surveys. I use distance-detection methods in my avian point count surveys while in the field, whereby I estimate the distance from me to all birds detected during a count to the nearest meter (calibrated by a laser rangefinder). Thus, I have distance estimates for every data point. In Exercise 2, I filtered bird observation to 150 m from observer. This is large distance and probably more than is reasonable for the dataset as it could introduce error, such as misidentified birds and potentially double counting of birds within localities due to overlapping count radii. As I wanted to test the method while ensuring I had enough species points to calculate the functional diversity, I decided to leave in those observation for the the first pass of this analysis in Exercise 2. In this exercise, however, I filtered the bird observations to passerines only detected within 50m or less of the observer during point counts. This is a much more reasonable distance and is in line with other approaches in passerine data collection methods using point counts. This filtering step reduced the number of observations in the bird-mammal input dataset to 1211 from the original 1668 species records and from 56 bird species to 51. I did not modify the small mammal data.

Figure 2. A simple 3-dimensional representation of the space containing the xy  (latitude and longitude) and z (elevation in meters) coordinates of the 16 localities used in this analyses created using the ‘scatterplot3d’ package in R.

Results

Using the results of the Moran’s I global test (Table 1.) in Exercise 1., we can reject the null hypothesis that there is no spatial autocorrelation present for a given functional metric across these localities if p-value is < 0.05. Based on these results, functional richness, functional originality, and the number of functional entities appear to be spatially autocorrelated. All other functional metrics are not spatially autocorrelated correlated across the localities within the extent of this study.

Table 1. Output from Moran’s I analysis of all functional metrics across all localities.

The results from Exercise 2. show that turnover is the primary driver of taxonomic Sørensen dissimilarity between all localities (Figure 3.). Unlike taxonomic dissimilarity, functional dissimilarity does not seem to be driven exclusively by either turnover nor nestedness components and the relationship is less clear. I did not see a major difference in distance decay when dissimilarity is plotted against either xy or xyz distance, thus I have only presented the results from the xyz distance analysis here. The results after adjusting the dataset were strikingly similar to the results I obtained in the previous analysis (Figure 4.). As in Exercise 2, we do see a triangle plot emerge. This shows that taxonomic dissimilarity can be both extreme and low in near localities, but only extreme in localities that are spatially separated to greater degrees. This may indicate some lower bound of taxonomic dissimilarity as distance increases.

Figure 3. Taxonomic dissimilarity and functional dissimilarity as a function of three-dimensional distance (latitude, longitude and elevation in meters) for communities of rodents, shrews, and passerines modeled using a power function. Triangle symbols denote pairwise comparisons of localities. The y-axis indicates dissimilarity, and its turnover and nestedness components, in species composition (A – C) and functional composition (D – F) between localities (measured using the Sorensen dissimilarity index), with higher values indicating more dissimilar communities. Also shown are the slope (b) and coefficient of determination (R2) for the fitted models.

Figure 4. Taxonomic dissimilarity and functional dissimilarity as a function of three-dimensional distance (latitude, longitude and elevation in meters) for communities of rodents, shrews, and passerines modeled using a power function after filtering the data to only include bird observations < 50 m from observer. Triangle symbols denote pairwise comparisons of localities. The y-axis indicates dissimilarity, and its turnover and nestedness components, in species composition (A – C) and functional composition (D – F) between localities (measured using the Sorensen dissimilarity index), with higher values indicating more dissimilar communities. Also shown are the slope (b) and coefficient of determination (R2) for the fitted models.

Interpretation & Significance

Functional richness (FRic), functional originality (FOri), and the number of functional entities appear to be spatially autocorrelated based on the results of the Moran’s I global analysis. FRic indicates reflects the amount of niche space filled by species in the community. In this analysis I primarily chose functional traits that correspond to diet, thus changes in species composition may modify the functional richness if those species consume drastically different resources. It makes sense that communities that have similar FRic would be geographically autocorrelated due to the environmental filtering along the elevational gradient, as communities closer to each other are most likely comprised of similar species that use similar dietary resources. FOri quantifies how changes in species abundances modify the functional redundancy between species (i.e., minimal functional distances among species pairs). Species tend to be functionally less original in the pool if they tend to share their traits more closely with other species. The interpretation here is a bit trickier, but it seems that localities that have similar abundances of particular species are autocorrelated.

Turnover appears to be the primary driver of taxonomic dissimilarity in the bird-mammal communities along this gradient. As turnover between communities is though to indicate environmental filtering processes structuring species composition, this pattern makes sense considering these localities are distributed along an elevation gradient with major differences in environmental conditions and habitat between localities at different elevations. Interestingly, we do see a triangle plot emerge (panels A and B in Figure 3.).  This shows that taxonomic dissimilarity can be both extreme and low in near localities, but only extreme in localities that are spatially separated to greater degrees. This may indicate some lower bound of taxonomic dissimilarity as distance increases. 

The fact that the same dissimilarity pattern emerges when I adjusted the dataset to only include bird observations within 50 m of the observer is interesting (Figure 4.) and somewhat of a relief. For one, it suggests that that my field sampling was thorough enough to capture similar species both near and far from observation points. More useful though, this sensitivity analysis shows that the dissimilarity patterns observed are robust to slight variations in the underlying data set. The numbers of bird species was reduced in the dataset by 5, and the total number of observations was reduced by 457. This did have an effect on functional richness, as the functional metric calculations used in the ‘mFD’ package take both species identity and the abundances of each species into account. However the overall pattern of dissimilarity remained the same between pairwise comparisons of sites across the elevation gradient despite the reduction of species data. 

The findings I have produced in this course are an important part of describing the pattern in functional diversity of these bird-mammal communities along the gradient of the Steens-Alvord system. These results tell part of the story and will be valuable as I connect functional diversity of these disparate taxa to underlying environmental habitat characteristics.

Learning Reflection

Over the course of these three exercises I increased my learning of using program r to perform several analyses and wrangle data. Specifically, I used the ‘mFD’ and ‘betapart’ packages to perform multivariate analyses in order to calculate estimates of both taxonomic and functional diversity across communities, as well as dissimilarity of these communities as a function of geographic distance in three dimensions. I did a great deal of data sorting, which was time consuming but helpful in the long run—both for my dissertation chapter and my increased understand of working in r. I learned that certain spatial analyses were not appropriate for my data given the structure of the dataset and the aggregate metrics I was interested in. These techniques included kernel density and autocorrelation function, for example. I spent a great deal of time at the end of the course attempting to perform interpolation techniques on habitat data associate with my species observation data. Specifically I wanted to assess origins techniques to interpolate a habitat surface across locality landscapes from discrete habitat point data I have. Ultimately, I was unable to accomplish this in r, though I think this speaks more to my limitations that the program. I also failed at attempting to do this in QGIS before running out of time at the end of the course. I plan to continue attempting to achieve this goal.

Future Directions & Techniques

The work I have conducted in this course has been beneficial in that I have described spatial patterns of taxonomic and functional diversity in bird-mammal communities across the Steens-Alvord elevation gradient. This is a necessary first step for the work I am planning to do for this chapter of my dissertation. Ultimately I also plan to investigate the relationship between these patterns and covarying changes in habitat heterogeneity across elevation. To do this I will utilize habitat data I have calculated along small mammal trapline (i.e., structural complexity indices derived from desecrate habitat quadrate data collected in the field), as well as remotely sensed data I have pulled in using ArcGIS. I am interested in trying regression kriging and Empirical Bayesian kriging to develop habitat complexity surfaces that could be used as predictors in models that describe the influence of habitat heterogeneity an available area by elevation to functional metrics of these communities. Thus, my work continues. 

Research Question

My question and goal for the term stayed consistent across the term. I did drop one environmental driver, swell height, due to time constraints with downloading and extracting data. I also filtered my focus of the data from all years collected to just the month of the year with the most whale sightings. This month occurred during an El Nino year, so my hypotheses and initial goals were still applicable. Due to this shift in examining just the ten days, I stopped using the boundaries of the marine sanctuaries in my analysis in exercises 2 and 3; however, I did still use the clipped whale locations to those sanctuaries.

The original question was: The spatial problem I wanted to examine was the impact of ENSO on environmental drivers for fin whale area restricted search (foraging) behavior. This question was eventually broken down to what is the impact of ENSO on the spatial distribution of fin whales from August 1-10, 2016?

Data Description

The fin whale data used was from the Whale Habitat, Ecology, and Telemetry lab. The fin whales were clipped within the three marine sanctuaries, Cordell Bank, Monterey Bay, and the Greater Farallones, with a behavior state of 2 (area restricted search). The fin whale points have a resolution of 3 locations per 8 hours; however, some only transmitted one location for the day. The fin whale data was collected in 2004, 2006, and 2014-2018 during the summer-fall months. The whale locations were collected using Argos satellite tags and processed through a Bayesian switch state-space model, which produced regularized tracks and assigned behavior classifications due to the characteristics of the points.

All the area restricted search points had 714 point locations that were recorded daily with some uneven gaps. Their northernmost latitude is 38.99569 and their southernmost latitude is 35.54629. Their eastern-most longitude is -121.4425 and their westernmost longitude is -124.25.

When I restricted it to just August 1-10, 2016, the northernmost latitude is 37.82975 and the southernmost latitude 36.57525. The easternmost longitude is -122.0430 and the westernmost longitude is -123.4833.

SST: SST, GOES Imager, Day and Night, Western Hemisphere, 2000-2020 (1-Day Composite) from ERDDAP. This dataset had a resolution of 0.05 for latitude and longitude.

Chlorophyll-a: Chlorophyll-a, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (1-Day Composite) from ERDDAP. The dataset has a resolution of 0.0416 for latitude and longitude.

Disclaimer: Some chlorophyll-a concentration data was fabricated due to internal issues with the nibble program. I recommend using a different dataset for chlorophyll-a if someone were to recreate this analysis.

Figures 2-5. Chlorophyll-a concentration and sea surface temperature at the latitude and longitude of the fin whale locations were recorded between August 1-10, 2016.

Hypotheses

1. Exercise 1
   1. During cold modes, the spatial pattern of whale area restricted search will be clustered in areas with low SST, high chlorophyll-a concentration, and higher swell. I expect the clusters of area restricted search to be tighter in those conditions due to them needing to travel shorter distances to find the best locations in the habitat for feeding.
2. Exercise 2
   1. Cold sea surface temperature and high chlorophyll-a concentration in the central California coast area promote (enhances) fin whale area restricted search locations.
   1. Warm sea surface temperatures and low chlorophyll-a concentration in the central California coast area limits (reduces) fin whale area restricted search locations.
3. Exercise 3
   1. Based on the distribution of the whales and environmental drivers, the model will produce an output with the similar spatial distribution of whales under similar conditions for sea surface temperature and chlorophyll-a concentration.

Approaches

Point Pattern Analysis

In Exercise 1, I tried multiple methods to assess if the recorded whale locations were in a cluster or dispersal pattern. I also conducted some preliminary data visualization to examine where the patches were before statistical tests were applied. I had attempted to make a k-cluster test in python prior to this class, so I attempted that method first and moved on to Average Nearest Neighbor afterward when those results were difficult to assess in later years. This test was the most useful because the ratio value determined if the data was trending towards dispersal or clustering. I had extra time, so I also conducted point and kernel density on the points. While these helped generate future questions or focus points, they were not helpful for this exercise’s goal.

Cross-Correlation

My initial goal in the second exercise was to examine the autocorrelation between the two environmental drivers, sea surface temperature and chlorophyll-a concentration, and the whale locations. While this was interesting to examine, due to the nature of the telemetry data and the lag component of the autocorrelation and cross-correlation functions, I had difficulty interpreting the results, and it pushed my knowledge of R and statistics to the edge. Dr. Jones and I developed a personalized test using the kernel density value of the August 1-10, 2016, whales and an interpolated trend of the environmental data recorded during that time. This exercise improved my ability to problem solve and find personalized workarounds for my data that can still be understood and replicated by others. This problem helped me break down the larger question into smaller chunks that were easy to accomplish with my knowledge of ArcGIS Pro analysis tools.

Modeling

For the final exercise, my goal was to run a species distribution model that specifically examined the parameter sensitivity and impact on the spatial pattern of the whale locations. Due to the presence-only data, I started my process with the MaxEnt software but quickly ran into an issue because it was no longer supported by R studio. To counter this, I installed and learned the basics of Maxnet, MaxEnt’s successor in R. Despite my data being perfect for that type of model, the results in the output did not make sense. I found the ArcGIS equivalent of MaxEnt and ran into an issue from previous exercises: difficulty interpreting the results. While examining other model options in ArcGIS Pro, I found a random forest tool that predicted the latitude and longitudes of predicted whale locations based on the environmental driver parameters. From here, I devised some code in R to divide the location of the whales into 0.25-degree grid cells to count the number of whales present in each cell for the observed and predicted. I found that the model was both under and over-predicting values between displaying the results and calculating their residuals.

Results

The average nearest neighbor calculations for all the location data used in this exercise have statistically significant distances in all but one year, 2014. 2004’s p-value, while statistically significant, should be viewed with caution as the sample size is very small and likely impacts the p-value. 2015-2017 have a strong correlation between their distances.

The 2004 and 2014 data are considered trending toward dispersion due to their nearest neighbor ratios being higher than 1. 2015, 2016, and 2017 are considered trending towards clustered due to their nearest neighbor ratios being less than 1 (see Table 1). 2004, 2014, and 2015 were El Nino years, and the latter part of 2016 and all of 2017 were La Nina years. Using the Nearest Neighbor and point density results, 2016 and 2017 confirm the hypothesis I tested.

All but one correlation test resulted in a statistically significant p-value. The test for chlorophyll-a and longitude was the only test to produce a confidence interval entirely in the negative range. The absolute value of the T statistic produced in this test is used to determine if the autocorrelation for a specific lag equals zero. A T value greater than 2 indicates the autocorrelation is not equal to zero. In the chl and lon test, depending on rounding conventions, this T statistic could indicate the autocorrelation is not equal to zero.

The highest density of whales occurs in the 13-14 degrees Celsius range with a few in the warmer range. The highest density of whales occurs in the 6 mg m-3 range with a few in the higher concentration range.

The random forest model in ArcGIS Pro produced predicted latitude and longitudes for the whales given the parameters of the SST and chlorophyll-a concentration data. When examined in a 0.25-degree cell, there grid cell 7 (-123, -122.7 and 37.1, 37.3) had the most whales with 53 observed and 125 predicted.

Based on the results from Figure 8, there are some aspects of the model that underperformed, the points below the line and confidence interval, and those that overperformed, the ones above the trend line.

Significance

The different results from the exercises help understand the relationship between location and environmental factors. While I focused on a specific month, I combined my oceanographic and biological knowledge with the results and interpreted the outputs constructively. The tools and knowledge gained from this project can be used by future handlers of the data to determine the next steps in spatial analysis and other relationships to examine.

Software-wise, this project was significant because I used several R and ArcGIS Pro combinations. Many complete their analysis in just one software, but I wanted to combine workflows. This desire expanded my problem-solving capabilities because I could draw from multiple sources to achieve the analysis tests.

My Improvements in Skills and Statistics

I initially started with working proficiency with R and ArcGIS Pro and novice skills in Python3. At the end of the term, I believe I am closer to an expert with ArcGIS Pro and R regarding problem-solving and creating custom workflows for the problem. I am now at working proficiency with Python3.

I gained beneficial experience in manipulating data and understanding why specific formats and data collection work with some analysis tools and not with others. I was working with telemetry track data, which resulted in presence-only values.

Evolving Questions and Future Techniques

I would like to make the plots and maps from exercise 2 and run the models used in exercise 3 on different temporal scales and compare the results of the models to the initial question asked and hypotheses. Based on the graph of the relationship between the predicted and observed whales, I would like to examine further where the model is under and overpredicting. Examining those locations and trends in the data may answer why the model produced the output it did.

I would also like to develop a better contingency plan for the fault in remote sensing data and how to fill gaps in the data extraction. I had several issues with the raster data that impacted the accuracy and replicability of this analysis. While I stand by the methods I used for this class project, given more time and resources, I think I could have found another workaround that would make the results publishable.

I would also include all the whale behavior state locations to explore a more comprehensive analysis and explanation regarding what influences foraging behavior locations and spatial patterns.

• The research questions that you asked.

• What is the spatial distribution of volcanic vents in Harrat Khaybar, western Saudi Arabia? Are there spatial trends in volcanic vent distributions among the eruptive phases? Does the spatial distribution differ at various spatial scales?
• Is there a spatial correlation between the locations of volcanic vents and structural features (e.g., fractures, faults, and fissures) in Carrán-Los Venados Volcanic Field (CLV) in southern Chile? Does the relationship between the two variables differ at different spatial scales?
• What factors could lead to the differences in spatial correlation between the two variables at various spatial scales?

• A description of the dataset you examined, with spatial and temporal resolution and extent.

I examined spatial and temporal datasets including volcanic vent locations, fracture sites, and eruption ages of volcanic vents in two basaltic volcanic fields, Harrat Khaybar (HK) in western Saudi Arabaia and Carrán-Los Venados (CLV) Volcanic Field in southern Chile. Vent location (point-like features) and age data for the two study sites were available through my previous work (mapping and categorizing volcanic vents in Harrat Khaybar) and the literature (Bertin et al. 2019) using satellite images (spatial resolution = 30 m). However, structural feature data (line-like features) were limited to the CLV due to limitation of digital data in the HK. The temporal resolution component is not applicable in this project because the natural time spans of these geologic features are too wide. Thus, I estimated an overall time-averaged eruption rate of one eruption per 4427 years for the HK and one eruption per 250 years for the CLV assuming a Poisson distribution for inter-event times using available age data (Table. 1).

Table 1. Summary of the examined dataset in this project.

• Hypotheses: predictions of patterns and processes you looked for.

I predicted clustering of volcanic vents along the fractures. I expected that because structural features like faults are most likely to represent areas of crustal weakness at the time of magma emplacement.

• Approaches: analysis approaches you used.

1. Point pattern analysis in ArcGIS pro:
   • Multi-distance spatial cluster analysis (Ripley’s K function)
   • Average nearest neighbor (ANN) technique.
2. Quantitative and visual neighborhood analyses in ArcGIS pro and Excel:
   • Concentric buffers and the number of points that fall within each buffer.
   • Visual criteria.

• Results: what did you produce — maps? statistical relationships? other?

I could produce spatial probability maps and statistical relationships between my variables.

Ex. 1:

*The ANN tool is useful for evaluating the overall spatial patterns while the Ripley’s K function is more useful for recognizing spatial patterns at various spatial scales. (i.e., it accounts for spatial variation in density with respect to distance).

*Kernel density estimations for every eruptive phase, except Age-4 that has too few vents for such analysis, and for all vents together showing that volcanism at this volcanic field tends to cluster on the central part of the field throughout the time. Overall, all vents together indicate an elongated area of high probability density, (between 10^-5 and 10^-4) along the center of the harrat.

Ex. 2 and 3:

Table 2. Summary of the total number of vents fall in each buffer size.

*The neighborhood analysis is useful for evaluating the spatial correlation between the two variables in the CLV. However, it does not capture the spatial correlation at various scales. The findings of the analysis indicate that there is no spatial correlation between the variables at all while it can be clearly seen that all volcanic vents are concentrated in the center and surrounded by the fractures.

What did you learn from your results?

• Spatial patterns of volcanic vents could differ in each eruptive stage and at various spatial scales, ranging in spatial distribution from clustered to dispersed or normally distributed.
• The spatial correlation between my two variables, volcanic vents and structural features, varies at different scales and from one field to another based on the geologic settings of each province.
• There are several other factors that can influence the distribution of the volcanic vents in basaltic volcanic provinces such as the type of plate tectonics, size and shape of magma chamber/source(s), magmatic flux rate, and the thickness of the crust, Which impact the spatial correlation between the two variables at different spatial scales.

• Significance. How are these results important to science? to resource managers?

• My results can:
  • Contribute to the scientific understanding of distributed volcanism, in particular the key controls of intraplate volcanic propagation and origin and migration of magma
  • Contribute to the development of volcanic hazard analysis and risk assessments for distributed volcanic fields.
  • Highlight that more data is needed to reveal the main factors that influence the spatial distribution of volcanic vents in distributed volcanic fields. A better understanding of the associated plate tectonics, modeling of magma sources and crustal thickness (profile), and better spatial and temporal records of both variables are needed since each field might have unique factors

• Software learning. Your learning: what did you learn about software (a) Arc-Info, (b) GIS programming in Python, (c) programming in R, (d) Modelbuilder in Arc,or (e) other?

I learned conducting different spatial analyses in ArcGIS Pro including multi-distance spatial cluster analysis (Ripley’s K function) and Average nearest neighbor (ANN) analysis to reveal the point pattern of volcanic vents in two distributed volcanic fields as well as quantitative and visual neighborhood analyses using concentric buffers to evaluate the spatial correlation between my two variables.

• Statistics learning. What did you learn about statistics, including (a) hotspot, (b) spatial autocorrelation (including correlogram, wavelet, Fourier transform/spectral analysis), (c) cross-correlation/regression (cross-correlation, geographically weighted regression [GWR], regression trees, boosted regression trees), (d) multivariate methods (e.g., PCA, multiple component analysis), (e) other techniques (change detection/confusion matrices, other)?

• I learned about statistics that:
  • Different techniques for hotspot analyses such as the ANN and Ripley’s K function could result in different levels of details (i.e., spatial resolution), where the ANN tool is useful for evaluating the overall spatial patterns while the Ripley’s K function is more useful for recognizing spatial patterns at various spatial scales. (i.e., it accounts for spatial variation in density with respect to distance).
  • Some parameters in the kernel density function may have significant impact on the final products such as the bandwidth, area shape, and number of points.

• Evolving question. How did the results of each analysis lead you to change/refine your question?  Write out the original question you stated at the beginning of the class and restate the question(s) you now plan to address.

• How is the spatial pattern of volcanic vents in the volcanic field of Khaybar related to the spatial pattern of regional structural features (faults, fissures, and fractures) via plate tectonics.
• To what extent do the other factors (plate tectonic settings, size and shape of magma chamber/source(s), magmatic flux rate, and the thickness of the crust) influence the distribution of volcanic vents in distributed volcanic fields?

• Future techniques. What techniques would you like to explore to answer your research questions in the future?

• I would like to perform modeling techniques to model magma sources and crustal thickness (profile) using geology and remote sensing (e.g., LiDAR) to assess the contribution of the potential factors to the distribution of volcanic vents in basaltic volcanic fields.

Background:   

The Tufted Puffin (Fratercula cirrhata), is an iconic seabird that provides a wide range of ecological, economic, and historically important services such as ecotourism for local communities and bringing marine derived nutrients to terrestrial habitats. Tufted Puffin populations on the Oregon Coast have declined dramatically from over 5,000 birds in 1989 to 500 birds in 2021 (Figure 1). In 2018, the Tufted Puffin Species Status Assessment (SSA) determined that factors related to breeding site conditions are one of the most probable causes of puffin decline; however, little is known about the specific characteristics of nesting habitat along the Oregon coast, or how it relates to their population demographics. My research project will directly address this knowledge gap by analyzing how Tufted Puffin breeding habitat has changed on the Oregon Islands National Wildlife Refuge over the past 50 years. 

Figure 1. Breeding Tufted Puffin Estimates from 4 Oregon Coast-Wide Surveys in 1979, 1988, 2008, and 2021. Coast-wide surveys obtained from USFWS.  

Research Questions: 

1) How is the spatial pattern of Tufted Puffin population occupancy related to the spatial pattern of vegetation cover in terms of burrowing habitat availability?  

2) How have these spatial patterns at burrowing sites changed over time? 

3) Is Tufted Puffin occupancy high when vegetation cover near breeding sites is high, and low when nearby vegetation cover is low? 

Datasets: 

The temporal extent of my research project is from 1979 to 2021, and the spatial extent is the 62 islands on Oregon Islands National Wildlife Refuge where Tufted Puffins have historically nested. For the purposes of this class and these exercises, I limited my spatial extent to 10 islands, and limited my temporal extent from 2009 to 2018.  

To examine percent-cover of vegetation on Oregon Islands refuge over time, I used the National Agriculture Imagery Program (NAIP) imagery because the resolution is 1m, which is fine-scale enough to observe changes in NDVI on my island sites. I obtained NAIP imagery in Oregon for the years 2009 and 2018, because both these years had photography that was available in full color and near infrared. The spatial extent of the NAIP imagery includes the entire state of Oregon, so all my study sites are encapsulated in this dataset.  

I also used the data from 4 Tufted Puffin Oregon coast wide surveys to compare Tufted Puffin population occupancy (Figure 2). These coast wide surveys were conducted in 1979, 1988, 2008, and 2021 to estimate the total population of Tufted Puffins in Oregon, and their distribution across islands.   

Figure 2. Breeding Tufted Puffin Estimate on the Oregon Coast from 1979, 1988, 2008, and 2021 coast-wide surveys.  

Hypotheses: 

1) The spatial pattern of Tufted Puffin population occupancy will be clustered in response to the clustered spatial pattern of percent cover vegetation. 

2) Suitable Tufted Puffin breeding habitat will have decreased from 2009 to 2018, with more areas being vulnerable to erosion and vegetative degradation.  

3)Tufted Puffin occupancy will be high in areas where vegetative cover is high, and low where vegetative cover is low. 

 I expect percent cover of vegetation to be clustered, because these islands have large areas of sloped rock slabs where there will be no vegetation, close to areas with soil and dense vegetation. I expect the spatial pattern of Tufted Puffin density to be clustered in response to areas with clustered percent cover of vegetation because Tufted Puffin like lush, vegetated areas with grasses, forbs, and shrubs. I expect that vegetation cover will have decreased from 2009 to 2018, with more areas being vulnerable to erosion and vegetative degradation because of more severe and frequent weather conditions related to climate change.  

Approaches: 

To evaluate the spatial pattern of Tufted Puffin occupancy across islands, I used the average Nearest-Neighbor Distance (NND). To take this analysis one step further and determine if the spatial pattern of Tufted Puffin occupancy varied at different spatial scales, I used the Multi-Distance Spatial Cluster Analysis (Ripley’s K) function. I also used a kernel density estimator to determine if there were Tufted Puffin hot spots and identify where those might be along the Oregon Coast.  

To examine the spatial relationship between Tufted Puffin occupancy and vegetation cover, I used multiple tools and steps. First, I downloaded and extracted polygon boundaries of each of the islands. Next, I downloaded 2018 and 2009 NAIP imagery into ArcGIS Pro and clipped the rasters to the extent of the polygon boundaries, so I was only analyzing the data I was interested in. Then, I used NDVI to quantify the vegetation cover on each island. After that, I used focal statistics to identify the mean NDVI value on each island. Finally, I plotted mean NDVI values against the Tufted Puffin occupancy of each island as a logistic regression in R.  

Finally, I used a confusion matrix to observe changes in vegetation over time on one island. I began by clipping 2 NAIP rasters from 2009 and 2018 of the island to the previously extracted polygon boundary. Then, I used the iso cluster unsupervised classification tool to classify each raster into 2 classes. Next, I used the combine rasters tool to observe changes between years. The attribute table contained 4 columns that represented: 1) vegetation that stayed the same, 2) vegetation gained, 3) bare earth that stayed the same, 4) vegetation lost.  The attribute table displayed the pixel values associated with each of these categories. By dividing each category’s number of pixels by the sum number of pixels in the raster, we could determine what percent each category was.  

Results:  

From the Ripley’s K and Nearest-Neighbor analysis, I determined that Tufted Puffin occupancy in Oregon is clustered. The kernel density estimator outputs a map displaying the hot spots of breeding Tufted Puffin occupancy on the Oregon Coast (Figure 3). There is a dense hotspot in Northern Oregon, and another dense hot spot in Southern Oregon. The NDVI function outputs a raster with values ranging from –1 to 1, which is used in many scientific applications (Figure 4). The logistic regression analyses output a graph depicting the NDVI values against breeding Tufted Puffin occupancy (Figure 5). I interpreted these results as a positive correlation between breeding bird occupancy and NDVI. The higher the NDVI is at sites, the higher probability there will be more breeding birds. 

From the unsupervised classification and combine tools, I obtained a map of Goat Island with 4 color classes, where the colors corresponded to 1) vegetation gained, 2) bare rock that stayed consistent 3) vegetation that stayed the same between years, and 4) vegetation lost (Figure 6). I then used the attribute table to calculate the percentages of the number of pixels in each class relative to the overall number of pixels. From these results, I interpreted the vegetation decreased on Goat Island from 2009 to 2018 by 23%. The results also show an increase in vegetation from 2009 to 2018 by 8% (Table 1).    

Figure 3. Hot spots of breeding Tufted Puffins on the Oregon Coast in 2021. 

Figure 4. NDVI output raster of Goat Island from 2009 NAIP imagery.  

Figure 5. Logistic Regression analyses of NDVI values and breeding Tufted Puffin occupancy.  

Figure 6. Supervised classification of 2009 and 2018 NAIP imagery of Goat Island, followed by a raster combination. Red=vegetation lost between years, green=vegetation gained, yellow=vegetation that stayed the same, beige=bare rock that stayed the same.  

What I learned from each analysis:

The Ripley’s K and hot spot analyses were very interesting because up until this class, I had mainly been focused on detecting changes on a temporal scale. However, these analyses helped me to reframe some of my initial questions to observe changes on a spatial scale as well. I learned that on a larger spatial scale, Tufted Puffin occupancy was clustered, and there was one Northern hot spot and one southern hot spot. This led me to consider other questions about why Tufted Puffins are not breeding in large quantities along the central coast of Oregon, and what site-specific or climatic factors might be associated with the hot spots. For Exercise 2, I determined the NDVI and plotted it against Tufted Puffin occupancy for high and low sites in a logistic regression analysis. Exercise 2 helped me to learn that Tufted Puffin occupancy is high where vegetation is high, and occupancy is low where vegetation is low. From here, I can further investigate the driving forces behind what might be causing vegetation to differ spatially at different sites. From the supervised classification exercise, I learned that utilizing the “combine” tool provides more information than the raster calculator tool alone, because we get 4 layers to display vegetation change.

Significance.

It is important for land managers to know where the hot spots are along the Oregon coast. Once we understand where high and low occupancy of Tufted Puffin are, we can begin to investigate what characteristics make successful breeding habitat, and why some sites might exhibit higher occupancy than others. For example, we can compare hot spots to “sinks,” and look at factors such as mean sea-surface temperature (SST), mean air temperature, distance to the mainland, amount of human disturbance, etc.

I also learned that there is a positive spatial relationship between Tufted Puffin occupancy and vegetation at breeding sites. Although managers will not be able to directly use the analyses I did in this class, I can build off these results, by incorporating more sites in my analyses, to give the logistic regression more statistical power. This spatial analysis will provide invaluable insight into the habitat requirements of Tufted Puffin- a species of greatest conservation need- in Oregon. Gaining a deeper understanding of the specific vegetative characteristics Tufted Puffin utilize during the breeding season can be used to further address conservation concerns, and guide refuge managers in decision making around habitat restoration.

Finally, the confusion matrix allowed me to quantify the amount of vegetation lost, the amount of vegetation gained, and identify where these processes are occurring. This is significant because it can better inform land managers about where habitat restoration might be effectively implemented. I anticipate that the proposed data collection and analysis will serve as a springboard for future conservation efforts, allowing managers to make comparisons with ecologically similar seabirds that also use the Oregon Islands National Wildlife Refuge for breeding.

What I learned (software):

I did the majority of my analyses in ArcGIS Pro, and after this course, I feel much more comfortable using this software. This was the first time I’ve explored ArcGIS Pro without following the steps of a strict tutorial, and I am very happy with how much I’ve learned and the progress I’ve made on my project. Most importantly, I learned that ArcGIS software can be tricky, but if the original plan does not work, there are lots of adjacent ways to address the same problem. This class helped me get into the habitat of talking my process out with other folks who are working on similar projects and google alternative solutions if I run into problems.

Aside from verbally communicating my results, I also became more skilled at visually communicating my results. One critical component to research I learned in this class was creating figures or graphs in Program R and Excel to better display the results of a spatial analysis.

What I learned (statistics):

One thing I learned about using hot spots was that a few outliers can dramatically skew the distribution of my data. It was very helpful for me to log-transform my data prior to using a kernel density analysis to work with more normally distributed data. I also learned a lot about GWR from talking with other students in this class, and determined I would like to use this approach for my own data further down the line. I might be able to compare Tufted Puffin occupancy with multiple variables, such as slope, elevation, vegetation, precipitation, temperature, and other climate data. The GWR tool in ArcGIS would also allow me to compare the Pearson’s correlation coefficient between my variables, which would help me decide which variables to include in candidate models.

Evolving question.

Initial questions from the beginning of class:

1) How is the spatial pattern of Tufted Puffin population density related to the spatial pattern of percent cover of vegetation, soil depth, elevation, and slope in terms of burrowing habitat availability?

 2) How have these spatial patterns at burrowing sites changed over the last 50 years?

Questions I now plan to address:

1. How is the spatial and temporal pattern of Tufted Puffin population occupancy related to the spatial pattern of vegetation, elevation, and slope at Tufted Puffin breeding sites?
2. How have these spatial patterns at burrowing sites changed over the last 50 years?
3. How do these vegetation changes relate to the site-specific, climatic, and environmental variables influencing Tufted Puffin habitat at different spatial scales?

I still plan to identify how spatial patterns at burrowing sites have changed over the last 50 years. However, rather than exploring this question on just a temporal scale, I also plan to look at differences at different spatial scales. I originally planned to compare each island to a previous version of the same island. Now, I am excited to compare each island to other spatially distributed islands within the same year. For example, I might explore how cooler temperatures associated with islands on the Northern Coast affect the Tufted Puffin occupancy differently than warmer temperatures and less precipitation on the Southern Oregon Coast. For this third question, I am planning to look at vegetation cover as my independent variable, and a suite of climate processes as my response varaibles.

Future Research

This project allowed me to gain a deeper understanding of the relationship between Tufted Puffins and vegetative cover. My next step will be to observe this on a longer temporal scale, and a larger spatial scale. I am planning to now incorporate a separate aerial photography dataset I obtained from USFWS to observe changes in vegetation over 4 decades, rather than just 1 decade. I plan to use ArcGIS Pro to georeference this dataset, so the pixels of each island overlap from year to year, to make my results more meaningful. I am also planning to use a supervised classification technique to classify ground cover as vegetation, bare soil, rock, or water. To further compare vegetation cover to the occupancy of breeding Tufted Puffins on each island, I will use a logistic regression.

Moving forward, I am also planning to investigate the relationship between topography and Tufted Puffin occupancy over time. I plan to use the same basic process as I did when analyzing the vegetative cover, but instead of using NAIP rasters, I will use DEM/DTM rasters. I plan to clip DOGAMI DEMs and DTMs to the same polygon boundaries of the islands and subtract rasters from different years. I will also use spatial statistics to find the median elevation and slopes of the islands and use a regression approach to display the results.

I would also like to explore using GWR when I am further along in my research to determine which variables, such as slope, elevation, vegetation, precipitation, and temperature have the greatest impact on Tufted Puffin occupancy. To better understand the climate processes happening at each site, I am planning to download rasters of SST, precipitation, temperature, etc. From NOAA weather service sites, and extract values at each Tufted Puffin colony site.