Author Archives: nicolatk

Nearest Neighbor Analysis on Woodpecker Nest Locations Within Salvage Logging Units

Exercise 3: Nearest Neighbor Analysis

Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

  • How are woodpecker nests clustered within survey units? (Exercise 1 and 3)
  • How does this clustering relate to salvage treatment units within the survey units? (Exercise 2 and 3)

Tools

Average Nearest Neighbor in ArcMap

The Average Nearest Neighbor tool measures the distance between each feature centroid and its nearest neighbor’s centroid location and averages the distances for a nearest neighbor ratio. If the ratio is less than the average for a hypothetical random distribution, the feature distribution is clustered. If the ratio is greater than a hypothetical random distribution average, the features are dispersed.

Near Distance in ArcMap

The Near tool measures near distances between one set of target features and another set of target features. It produces additional columns in the original shapefile containing distance measurements in units of the user’s choosing. The user can enable a location option displaying X and Y distances individually.

Data

For this analysis, I used 2016 and 2017 woodpecker nest point datasets clipped to each RMRS survey unit. I also used a polygon shapefile of 35 salvage harvest units within RMRS woodpecker survey units. I used another polgyon shapefile of the woodpecker survey units and a WorldView-3 1 m raster for supplementary data.

Nearest Neighbor Analysis Steps

  1. Export 2016 and 2017 nest points as one nest shapefile per RMRS survey unit for inputs into the Average Nearest Neighbor tool.
  2. Export salvage treatment polygons into three shapefiles for treatments 1, 2, and 3.
  3. Run the Average Nearest Neighbor tool in ArcMap with multiple inputs. I ran the tool on each 2016 and 2017 nest shapefile per survey unit, all 2016 and 2017 nests, all salvage units, and each salvage treatment type shapefile (see table below).
  4. Run the Near tool to produce near distances in meters for the distance of each nest to a salvage unit. I ran this tool for 2016 and 2017 nests using salvage unit centroids and salvage unit polygons, which produced different results as expected.
  5. Use Excel to create an Average Nearest Neighbor table for comparing 2016 and 2017 results.
  6. Use ggplot in R to plot near distance graphs for 2016 and 2017. These graphs display near distance distributions for nest points to salvage unit centroids and near distances for nest points to salvage polygon boundaries.

Results

Near Distances

I produced boxplots displaying near distance distributions for nest points. Sets of graphs are featured with and without the control nests for different visual interpretations.

Near Distances for Nest Points to Salvage Centroids (With Control Nests)

Near Distances for Nest Points to Salvage Centroids (Without Control Nests)

Near Distances for Nest Points to Salvage Polygons (With Control Nests)

Near Distances for Nest Points to Salvage Polygons (Without Control Nests)

Nearest Neighbor Results Table

Above: Nearest neighbor results for woodpecker nests in each survey unit in 2016 and 2017. The NN Ratio is a threshold for expected clustering or dispersion. NN Ratio values less than 1 indicate clustering and NN Ratio values greater than 1 indicate dispersion. In 2017, green cells indicate an increase in value and red cells indicate a decrease in value. NN Ratio cells with an increased value in 2017 indicate units where nests increased dispersion. NN Ratio cells with a decreased value in 2017 indicate units where nests increased clustering. Alder Gulch (Treatment 3), Lower Fawn Creek (Treatment 2), and Upper Fawn Creek (Treatment 2) demonstrated increased clustering in 2017. Big Canyon (Treatment 1), Crazy Creek (Treatment 1), and Sloan Gulch (Treatment 3) experienced increased nest dispersion in 2017. All control units experienced increased or present dispersion in 2017. The “All Nests” and salvage shapefile results were produced for comparisons and to evaluate how clustering/dispersion within datasets may affect clustering/dispersion of other datasets.

With these two techniques creating near distance values and a nearest neighbor table, we can implement a refined nearest neighbor analysis. This analysis examines nearest neighbor distances in combination with distances to the study boundary. Refined nearest neighbor uses standardized formulas integrating the distribution of observed nearest neighbor distances and the distribution of expected (random) nearest neighbor distances while factoring in distance to unit boundaries. In the table above, the NN Expected and NN Observed values will aid  refined nearest neighbor results.

Problems and Critique

Did this nearest neighbor analysis account for the target area size and shape? In the ArcMap Average Nearest Neighbor help documents, these assumptions are made:

  • “The equations used to calculate the average nearest neighbor distance index (1) and z-score (4) are based on the assumption that the points being measured are free to locate anywhere within the study area (for example, there are no barriers, and all cases or features are located independently of one another).”
  • “The average nearest neighbor method is very sensitive to the Area value (small changes in the Area parameter value can result in considerable changes in the z-score and p-value results). Consequently, the Average Nearest Neighbor tool is most effective for comparing different features in a fixed study area.”

Because of this, I’m not sure whether I can interpret these results properly based on the study design. We are surveying specially designated study units, not the entire burn area. Therefore, our sample calculations should not assume the entire area is available for clustering or dispersion. There is an option in ArcMap to input “Area” as an integer but not as a polygon shapefile (for survey and salvage units). To control for this, I calculated Average Nearest Neighbor on nest shapefiles for each survey unit instead of all nests together. However, as shown in the table above I also calculated Average Nearest Neighbor for all nests and different combinations of salvage units. These results may prove less reliable.

I would like to extend this analysis to a nearest neighbor technique adjusting for both the clustering/dispersion of the nests and the clustering/dispersion of the salvage units and survey units at the same time. The predetermined study design continues to require special consideration.

Multiple Buffer Distance Analysis on Woodpecker Nest Buffers Intersecting Salvage Harvest Areas

Exercise 2: Multiple Buffer Distance Analysis

Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

  • How are woodpecker nests clustered within survey units? (Exercise 1 and 3)
  • How does this clustering relate to salvage treatment units within the survey units? (Exercise 2 and 3)

Tool

Multiple Ring Buffer in ArcMap

This tool creates shapefiles of concentric circles around features based on user-input values. Users can dissolve buffers to create a single feature class per buffer distance. Buffer widths overlapping with features of interest can indicate spatial relationships between target subjects. In this case, intersecting woodpecker nest buffers with salvage harvest polygons may reveal trends in woodpeckers selecting nest sites closer to or farther from salvage units. An equation producing percent area of each nest buffer intersected by a salvage unit serves as an index.

Above: Buffers created in a test analysis at 15 – 50 meter intervals around nest point features.

Data

For this exercise I used 2016 and 2017 woodpecker nest point shapefiles. I created multiple ring buffer outputs for each shapefile to use in the analysis. I also used a polygon shapefile of 35 salvage harvest units included within the treatment woodpecker survey units. I used another polgyon shapefile of the woodpecker survey units and a WorldView-3 1 m raster for supplementary data.

Multiple Buffer Distance Analysis Steps

  1. Create separate point shapefiles for 2016 and 2017 nest points.
  2. Run the Multiple Ring Buffer tool in ArcMap on each shapefile at 50 meter intervals from 50 – 300 meters. The tool creates
  3. Use the Intersect tool in ArcMap to create a polygon shapefile of the buffers clipped to the salvage harvest units.
  4. Use the Dissolve tool in ArcMap to merge overlapping buffer polygons of the same type for the same nest.

Above: The green polygons indicate areas where the Intersect tool identified overlap between the buffers and salvage unit polygons. The tool creates a polygon shapefile of the overlapping areas.

Above: The final result of six buffers at 50 m intervals around nest points intersected with salvage harvest units. Each color represents an individual polygon in a shapefile. The polygons are given size information and used to calculate percent overlap of nest buffers with salvage units.

5. Use the XTools Pro extension to attribute size information to each intersected buffer shapefile for area in square meters.

6. Create a new field in each intersected buffer attribute table for percent area of the buffer intersected by a salvage unit.

7. Use the field calculator to create a formula for percent buffer area intersected: (Area field/Complete buffer size)*100

8. Export each intersected buffer table to Excel, including the salvage treatment type column and the percent buffer area intersected column for further analysis. Transfer the salvage treatment type and corresponding percent buffer area intersected columns to a combined Excel file with a sheet for every buffer distance.

9. Add zero values to each sheet for the nests falling within a control unit. The control nests act as a fourth treatment and should be included in the results.

10. Use ggplot2 in R to extract data from each sheet and create box plots for each buffer distance.

Above: Excel table of X and Y inputs for boxplots showing percent of the complete buffer intersected by a salvage harvest unit. Each buffer distance from 50 – 300 m has a sheet containing columns for these values.

Results

I generated graphs for each of the six buffer intervals (50-300 m at 50 m intervals) in 2016 (pre-salvage) and 2017 (post-salvage)  for 12 total graphs. I presented the 2016 and 2017 results for each buffer distance side by side below. In each graph, the unlabeled grouping of points to the left of salvage treatment type 1 represents nests in the control area, or nests 100% inside salvage treatment 0. Visual analysis reveals interesting patterns about woodpecker nest site selection when considering the silvicultural prescriptions designating salvage treatment types. Below is a reminder of the salvage treatments:

Treatment 1 harvests the most trees overall but retains the most large diameter trees with spacing for Lewis’s woodpeckers. Treatment 2 harvests a moderate number of trees, retaining less large diameter trees but more medium and small diameter trees. Treatment 3 harvests a limited number of trees but retains barely any large diameter with a heavy focus on small diameter for white-headed woodpeckers. The control treatments do not harvest any trees.

An obvious downfall of the following graphs is that as distance from the nest increases, percent of the nest buffer intersecting a salvage polygon will decrease. There is an overall decrease in mean and midrange values for intersecting area as the buffer distance increases. However, some significant points emerge:

  • Nest distribution in Treatment 1 units generally increases in breadth between 2016 and 2017. Meaning, in 2016 the distribution of woodpecker nest distances from a salvage unit centered more closely around a mean distance near 30 – 50%. In 2017 the values appear to spread out with less preference towards a specific distance from a salvage unit.
  • Nests in Treatments 2 and 3 units generally decrease in percent area intersected by a salvage unit from 2016 to 2017. Meaning, after the salvage harvest, woodpeckers are selecting nest sites farther from Treatments 2 and 3.

50 Meter Buffer Results

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100 Meter Buffer Results

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150 Meter Buffer Results

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200 Meter Buffer Results

 

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250 Meter Buffer Results

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300 Meter Buffer Results

Problems and Critique

I would perform this analysis again with larger buffer sizes and greater buffer intervals. I am not convinced the buffer size I chose for this exercise captured significant information at this scale. I created 10 buffers from 100 – 1000 m for a future analysis. Buffer size should be determined by assumed travel and foraging distances for each woodpecker species. The 50 – 300 m scale may not be a great enough distance for local trends to develop in the data. I chose these distances because the belt transects for the woodpecker point count surveys are 200 – 300 m apart to avoid interfering with birds on neighboring transects. I thought this would be a comparable scale for the buffer analysis.

This analysis fails to address or quantify the control nests because they are too far away from salvage units for their buffers to intersect. In Exercise 3, a near analysis in ArcMap can quantify trends in control nest distances from salvage areas.

In the future I would like to produce a statistics table for each 2016 and 2017 buffer distance displaying mean, standard deviation, and other metrics for more than a visual analysis.

Geospatial Analyses Examining Woodpecker Nest Site Selection and Postfire Salvage Logging

Research Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

  • How are woodpecker nests clustered within survey units? (Exercise 1 and 3)
  • How does this clustering relate to salvage treatment units within the survey units? (Exercise 2 and 3)

After evaluating my data with several spatial analyses methods, this research question still seems applicable to my project goals. A possible revision to this research question is to include phrasing about the internal spatial characteristics of the nest and salvage harvest location datasets aside from their relationship to each other.

Dataset

This project uses postfire datasets targeting the 2015 Canyon Creek fire complex on the Malheur National Forest in eastern Oregon. A salvage logging operation occurred in the burn area in July 2016. My geospatial analyses examine the relationships between woodpecker nest site locations and salvage harvest unit locations before and after the logging occured. This will lead to correlating woodpecker population dynamics with salvage treatment effects. Woodpeckers are an ideal indicator species for postfire ecosystem monitoring due to their heavy reliance on burned snags for bark-beetle foraging and cavity-nesting. Their population dynamics indicate habitat quality for all snag-dependent wildlife. Previous research indicates woodpeckers exhibiting species-specific habitat preferences toward burned forest variables like stand density, diameter class, height, and snag decay class. 2016 pre-salvage and 2017 post-salvage lidar datasets are in processing for eventual correlation between these 3D forest structure variables and woodpecker nest site selection before and after harvest. However, the spatial analyses featured in this project focused on the intial task of relating 2D nest and salvage unit locations.

My research is in conjunction with a Rocky Mountain Research Station study examining salvage logging effects on three woodpecker species in the Canyon Creek complex. In 2016 and 2017 I led crews on this project collecting extensive postfire woodpecker occupancy and nest productivity datasets for black-backed, white-headed, and Lewis’s woodpecker populations. This resulted in a 148-nest dataset for 2016 and 2017, representing woodpecker populations before and after salvage logging. A polygon shapefile outlining ten RMRS woodpecker point count survey units serves as the area of interest (6 treatment, 4 control). Within the 6 treatment units, another polygon shapefile outlining 35 salvage harvest units indicates treatment areas. Three silvicultural prescriptions replicating optimal habitat types for each woodpecker species designate target salvage variables like average post-harvest stand density and diameter class. Each salvage unit adheres to one of these three harvest prescriptions. Supplementary geospatial data includes a 2015 WorldView-3 1 m raster and ArcGIS basemaps.

Image result for canyon creek fire oregon

Above: The 2015 Canyon Creek Fire burning near John Day, OR.

Image result for black-backed woodpecker              Image result for white headed woodpecker             Image result for lewis's woodpecker

Black-backed woodpecker (Picodies arcticus)     White-headed woodpecker (Picoides albolarvatus)                       Lewis’s woodpecker (Melanerpes lewis)

Above: The three target woodpecker species. All species are Oregon Sensitive Species. The white-headed woodpecker is a U.S. Fish and Wildlife Species of Concern.

Above: Survey units and corresponding salvage treatments for each unit. Each treatment type is based on a silvicultural prescription designed to benefit target woodpecker species. Based on previous research, each target species demonstrates preferred habitat characteristics in variables like stand density, diameter class, height, and decay class.

Above: The three salvage treatment types and the control treatment depicted as overhead stem maps. Each polygon represents a snag of indicated diameter class. The salvage treatments retain more trees of smaller diameter moving from Treatment 1 to Treatment 3, with the control units retaining all trees.

Above: A salvage treatment in the Crazy Creek woodpecker survey unit (Treatment 1).

Above: An early project map showing rough locations of Rocky Mountain Research Station woodpecker survey units. Since this map was created, two more survey units were added for 10 total (6 treatment, 4 control). This map effectively shows the woodpecker survey units occupying areas of highest burn severity.

Above: The Canyon Creek fire complex as a false color WorldView-3 1 m raster. The area of interest includes 10 survey units in blue, labeled with yellow text (6 treatment, 4 control). This visual orients the survey units to an area in eastern Oregon southeast of John Day and Canyon City. The false color image displays healthy vegetation as red, with the darkest areas displaying high burn severity. The survey units are found within some of the highest burn severity areas in the fire complex.

Above: A close-up of the 35 salvage treatment polygons outlined in red and labeled with white text. Control units lack red salvage polygons. This image does not include Overholt Creek.

Above: A subset of the 2016 (orange) and 2017 (pink) nest points featuring survey (gold) and salvage unit (green) polygons.

Hypotheses

I expected to see dispersed nests in 2016 with possible trends indicating species habitat preferences. Previous research indicates species-specific preferences for certain forest habitat variables. Black-backed woodpeckers prefer dense, small-diameter stands for foraging and nest excavation. White-headed woodpeckers prefer a mosaic of live and dead variable density and diameter stands for pine cone foraging. Lewis’s woodpeckers prefer tall medium to large-diameter stands for aerial foraging maneuvers. I expected to see nest sites in both years clustered in areas with these forest structures. In 2017 I also expected to see nest sites clustered near salvage treatments implemented for each species. Overall I expected the control units to exhibit nest dispersal and high woodpecker activity.

Approaches

Exercise 1: Multi-Distance Spatial Cluster Analysis (Ripley’s K)

Multi-Distance Spatial Cluster Analysis (Ripley’s K) analyzes point data clustering over a range of distances. Ripley’s K indicates how spatial clustering or dispersion changes with neighborhood size. If the user specifies distances to evaluate, starting distance, or distance increment, Ripley’s K identifies the number of neighboring features associated with each point if the neighboring features are closer than the distance being evaluated. As the evaluation distance increases, each feature will typically have more neighbors. If the average number of neighbors for a particular evaluation distance is higher/larger than the average concentration of features throughout the study area, the distribution is considered clustered at that distance.

K Function Graphic

 

 

 

 

 

 

 

 

 

Referenced from ArcGIS Desktop 9.3 Help (http://resources.esri.com/help/9.3/arcgisdesktop/com/gp_toolref/spatial_statistics_tools/how_multi_distance_spatial_cluster_analysis_colon_ripley_s_k_function_spatial_statistics_works.htm)

Exercise 2: Multiple Buffer Distance Analysis

This tool creates shapefiles of concentric circles around features based on user-input values. Users can dissolve buffers to create a single feature class per buffer distance. Buffer widths overlapping with features of interest can indicate spatial relationships between target subjects. In this case, intersecting woodpecker nest buffers with salvage harvest polygons may reveal trends in woodpeckers selecting nest sites closer to or farther from salvage units. An equation producing percent area of each nest buffer intersected by a salvage unit serves as an index.

Exercise 3: Nearest Neighbor

The Average Nearest Neighbor tool measures the distance between each feature centroid and its nearest neighbor’s centroid location and averages the distances for a nearest neighbor ratio. If the ratio is less than the average for a hypothetical random distribution, the feature distribution is clustered. If the ratio is greater than a hypothetical random distribution average, the features are dispersed.

Results

Exercise 1: Multi-Distance Spatial Cluster Analysis (Ripley’s K)

 

 

 

 

Above: Notable clustering results from Ripley’s K analysis. Most sample sizes were too small to produce significant results, but Big Canyon and Crawford Gulch in 2016 and Lower Fawn Creek in 2017 showed evidence of clustering.

Exercise 2: Multiple Buffer Distance Analysis

Above: The difference in results between 50 and 100 meter buffer distances for the 2016 nest dataset demonstrate the utility of graphs created from buffer analysis. These results indicate that woodpeckers consistently choose to place their nests in areas completely surrounded by salvage treatment 3. The results also indicate that woodpeckers consistently place their nests within varying distances of salvage treatment 1 with trends indicating average nest placement near but not completely within those salvage treatments.

Exercise 3: Nearest Neighbor

Above: Nearest neighbor results for woodpecker nests in each survey unit in 2016 and 2017. The NN Ratio is a threshold for expected clustering or dispersion. NN Ratio values less than 1 indicate clustering and NN Ratio values greater than 1 indicate dispersion. In 2017, green cells indicate an increase in value and red cells indicate a decrease in value. NN Ratio cells with an increased value in 2017 indicate units where nests increased dispersion. NN Ratio cells with a decreased value in 2017 indicate units where nests increased clustering. Alder Gulch (Treatment 3), Lower Fawn Creek (Treatment 2), and Upper Fawn Creek (Treatment 2) demonstrated increased clustering in 2017. Big Canyon (Treatment 1), Crazy Creek (Treatment 1), and Sloan Gulch (Treatment 3) experienced increased nest dispersion in 2017. All control units experienced increased or present dispersion in 2017.

Significance

I am processing two lidar datasets of the study area from 2016 and 2017. These datasets were acquired before and after the salvage logging treatments occurred. I will produce forest metrics such as stand density, diameter class, and height in salvage and survey units. I will then correlate geospatial and statistical properties of the nest datasets to quantified forest variables affecting woodpecker nest site selection. I will examine trends between 2016 and 2017 nest selection to understand the effects of harvest treatments on woodpecker populations. At least two more years of woodpecker data will exist for 2018 and 2019, so future research will add these datasets to the analyses. I would like to see a machine learning algorithm developed from this study that could predict areas of optimal habitat suitability for snag-dependent wildlife species. Postfire wildlife habitat prediction will be crucial to resource managers as wildfires increase in the coming decades alongside accelerated landscape restoration.

This spatial problem is important to science and resource managers as climate change amplifies wildfire effects. Using 3D remote sensing datasets for resource management is the future trend across all disciplines. Increased wildfire activity around the world necessitates cutting-edge methods for active fire and postfire ecosystem quantification. In the Blue Mountains ecoregion in eastern Oregon, Rocky Mountain Research Station, Malheur National Forest, and Blue Mountains Forest Partners rely on this project’s lidar quantification for their research and management decisions. Determining acceptable levels of salvage harvest for wildlife habitat affects whether government agencies and rural economies in this region will allow small businesses to profit from sustainable harvest operations. Science overall will benefit from the continued investigation of wildlife response to anthropogenic disturbance, specifically postfire forest management decisions and the controversial practice of salvage logging.

Learning Software

For my analyses I used mostly ArcMap, ArcGIS Pro, and R. I am very familiar with ArcMap, but it was my first time using ArcGIS Pro extensively. I enjoyed applying my R programming experience to my own datasets and creating visual aids for my research.

Learning Statistics

The statistical analyses I implemented for my research did an adequate job characterizing my datasets. However, I would try a different set of analyses that fit my small sample sizes better. A spatial autocorrelation test may support these analyses. I used R heavily in my Ripley’s K and buffer analyses to create graphs

Multi-Distance Spatial Cluster Analysis for Woodpecker Nests (Ripley’s K)

Exercise 1: Multi-Distance Spatial Cluster Analysis (Ripley’s K)

Questions 

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

  • How are woodpecker nests clustered within survey units? (Exercise 1 and 3)
  • How does this clustering relate to salvage treatment units within the survey units? (Exercise 2 and 3)

Tool

Multi-Distance Spatial Cluster Analysis (Ripley’s K) in R

Multi-Distance Spatial Cluster Analysis (Ripley’s K) analyzes point data clustering over a range of distances. Ripley’s K indicates how spatial clustering or dispersion changes with neighborhood size. If the user specifies distances to evaluate, starting distance, or distance increment, Ripley’s K identifies the number of neighboring features associated with each point if the neighboring features are closer than the distance being evaluated. As the evaluation distance increases, each feature will typically have more neighbors. If the average number of neighbors for a particular evaluation distance is higher/larger than the average concentration of features throughout the study area, the distribution is considered clustered at that distance.

K Function Graphic

 

 

 

 

 

 

 

 

 

 

 

 

 

Referenced from ArcGIS Desktop 9.3 Help (http://resources.esri.com/help/9.3/arcgisdesktop/com/gp_toolref/spatial_statistics_tools/how_multi_distance_spatial_cluster_analysis_colon_ripley_s_k_function_spatial_statistics_works.htm)

Data Description

My polygon dataset includes 10 survey units (6 treatment, 4 control) totaling over 7,000 ac. Each survey unit is between 397 and 1154 ac2. 34 salvage logging units are dispersed throughout the 6 treatment units. The salvage units are characterized by three silvicultural prescriptions replicating preferred postfire habitat for the three target woodpecker species: black-backed, white-headed, and Lewis’s woodpeckers. The 4 control survey units and the unlogged landscape in the 6 treatment units act as the undisturbed control habitat.

In 2016 and 2017, belt transects with corresponding avian point counts were surveyed in each of the 10 survey units. These surveys detected woodpecker occupancy and nest locations based on audio playback and nest searching methodology. Woodpecker nest datasets were developed from these surveys for pre-salvage (2016, n = 71) and post-salvage (2017, n = 77) conditions. I wanted to run Ripley’s K on the two datasets separately to determine differences in nest clustering before and after the salvage treatments. In the future, with successive datasets collected in 2018 and 2019, I can analyze clustering trends up to three years after salvage logging. I will also integrate 3D forest structure variables from pre- and post-salvage lidar datasets.

A subset of the 2016 and 2017 nest datasets picturing clockwise from top right: East Fork Canyon Creek (Ctrl), Wall Creek (Ctrl), Alder Gulch (Trt), Lower Fawn Springs (Trt), Upper Fawn Springs (Trt).

By using Exercise 1 to determine how nests are clustered within the study units, I can use this clustering to inform my Exercises 2 and 3, which should reveal how the clustering relates to the salvage harvest units.

Ripley’s K Steps

  1. Export nest points as their own shapefile. My preliminary point dataset includes both nest points and vegetation survey points, so I needed to isolate only nest points. This will indicate how nest points cluster within study units.
  2. Further export nest points by survey unit. Isolate nests in each survey unit as their own shapefiles. Running Ripley’s K on 2016 and 2017 nests as a whole will not be useful, since clustering across an entire nest dataset will reflect the 10 survey units selected for this study. In that case, Ripley’s K would falsely demonstrate high clustering within those 10 survey units. In reality, the nests are only clustered here because the surveys intentionally restricted nest detection to these areas instead of across the entire Canyon Creek fire complex. I ran Ripley’s K on all the 2016 and 2017 nests and the output proved true to this phenomenon, indicating statistically significant clustering at smaller distances:

  1. Use R to run Ripley’s K on each survey unit’s 2016 nest shapefile.
  2. Use R to run Ripley’s K on each survey unit’s 2017 nest shapefile.

Below is the example script for Ripley’s K using the 2016 Alder Gulch survey unit. For 2017 I  changed the variable names to 2017:

library(spatstat)
library(rgdal)
library(maptools)
library(ggplot2)
library(sp)
library(nlme)
library(rpart)
library(readxl)
library(raster)
setwd(“N:/AIS_Blue/Woodpeckers”)
getwd()
AGnests2016 <- readOGR(“./data”, layer = “AG_2016_nests”)
plot(AGnests2016)
class(AGnests2016)
AGnests2016.ppp <- as(AGnests2016,”ppp”)
n <- 100
AGnests2016RK <- envelope(AGnests2016.ppp, fun= Kest, nsim=n, verbose=F)
plot(AGnests2016RK)

I gave the option to run either 100 or 1000 iterations. The difference is that the confidence interval (grey area in the output graphs) widened with 1000 iterations, shown below for the Big Canyon survey unit:

Below are the Ripley’s K results for each survey unit in 2016 and 2017 over 100 iterations. The accompanying visual plots display nest point distribution in each survey unit. Some survey units did not have large enough sample sizes for the tool to function correctly. Notable results from a visual analysis of each graph include nest clustering in Big Canyon (Treatment 1) and Crawford Gulch (Control) in 2016 and Lower Fawn Creek (Treatment 2) in 2017.

Alder Gulch 2016 (n = 5)

 

Alder Gulch 2017 (n = 7)

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Big Canyon 2016 (n = 13)

 

Big Canyon 2017 (n = 10)

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Crazy Creek 2016 (n = 10)

Crazy Creek 2017 (n = 11)

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Crawford Gulch 2016 (n = 12)

 

Crawford Gulch 2017 (n = 8)

 

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East Fork Canyon Creek 2016 (n = 3); Not surveyed in 2017

 

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Lower Fawn Creek 2016 (n = 8)

 

Lower Fawn Creek 2017 (n = 14)

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Overholt Creek 2017 (n = 4); Not surveyed in 2016

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Sloan Gulch 2016 (n = 7)

 

Sloan Gulch 2017 (n = 7)

 

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Upper Fawn Creek 2016 (n = 4)

 

Upper Fawn Creek 2017 (n = 5)

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Wall Creek 2016 (n = 9)

Wall Creek 2017 (n = 11); Cannot import figures due to unresolved maximum file size error.

Problems/Critique

Because of the small sample size for each of the survey units (as few as 4 nests in some years/units), my Ripley’s K output graphs appeared blocky instead of continuous. Notice how continuous the graphs appeared when I ran all 2016 nests. Overall this analysis did not perform well with these datasets. I am also unclear how edge effects were factored into this particular analysis. There may be more parameters I could define in the code when running this analysis in R. For example, I did not manually specify distances in the R code.

Ripley’s K requires (X,Y) coordinates for each point location. I first tried to perform this analysis in ArcMap. However, my woodpecker nest point shapefiles contain UTM coordinates divided into two separate fields for northing and easting. This caused a problem when the Ripley’s K tool in ArcMap asked for the dependent variable and I could only select one field, northing or easting. Therefore, I needed to run the Ripley’s K tool in RStudio instead.

The above Step 2 explains another issue with trying to analyze all nests at once, but I was able to resolve it by individually isolating each survey unit. Nest dataset analyses must also address a temporal component related to the salvage logging. 2016 nests must be analyzed separately from 2017-2019 nests, but 2017-2019 nests can be analyzed either by year or as a group.

Monitoring Postfire Salvage Logging Effects on Woodpecker Population Dynamics

Research Question

How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?

Dataset

This project will use datasets targeting the 2015 Canyon Creek fire complex on the Malheur National Forest in eastern Oregon. A salvage logging operation occurred in the burn area in July 2016. My research is in cooperation with a Rocky Mountain Research Station study examining salvage logging effects on three woodpecker species in the Canyon Creek complex. In 2016 and 2017 I led crews on this project collecting extensive postfire woodpecker occupancy and nest productivity datasets for black-backed, white-headed, and Lewis’s woodpecker populations. This resulted in a 148-nest dataset for 2016 and 2017, representing woodpecker populations before and after salvage logging. A polygon shapefile outlining ten RMRS woodpecker point count survey units serves as the area of interest (6 treatment, 4 control). Within the 6 treatment units, another polygon shapefile outlining 34 salvage harvest units indicates treatment areas. Three silvicultural prescriptions replicating optimal habitat types for each woodpecker species designate salvage variables like post-harvest stand density and diameter. Each salvage unit adheres to one of these three harvest prescriptions. 2016 pre-salvage and 2017 post-salvage lidar datasets are in processing for eventual correlation between 3D forest structure variables and woodpecker nest site selection before and after harvest. Supplementary geospatial data includes a 2015 WorldView-3 1 m raster and ArcGIS basemaps.

Image result for canyon creek fire oregon   

Above: The 2015 Canyon Creek Fire burning near John Day, OR.

Above: The Canyon Creek fire complex as a false color WorldView-3 1 m raster. The area of interest includes 10 study units in blue, labeled with yellow text (6 treatment, 4 control). This visual orients the survey units to an area in eastern Oregon southeast of John Day and Canyon City. The false color image displays healthy vegetation as red, with the darkest areas displaying high burn severity. The survey units are found within some of the highest burn severity areas in the fire complex.

Above: A close-up of the 34 salvage treatment polygons outlined in red and labeled with white text. Control units lack red salvage polygons. This image does not include Overholt Creek.

Above: A subset of the 2016 and 2017 nest points featuring survey and salvage unit polygons.

Hypotheses

I expect to see dispersed nests in 2016 with possible trends indicating species habitat preferences. Previous research indicates species-specific preferences for certain forest habitat variables. Black-backed woodpeckers prefer dense, small-diameter stands for foraging and nest excavation. White-headed woodpeckers prefer a mosaic of live and dead variable density and diameter stands for pine cone foraging. Lewis’s woodpeckers prefer tall medium to large-diameter stands for aerial foraging maneuvers. I expect to see nest sites in both years clustered in areas with these forest structures. In 2017 I also expect to see nest sites clustered near salvage treatments implemented for each species. Overall I expect the control units to exhibit nest dispersal and high woodpecker activity.

Image result for black-backed woodpecker              Image result for white headed woodpecker             Image result for lewis's woodpecker

Black-backed woodpecker (Picodies arcticus)     White-headed woodpecker (Picoides albolarvatus)                       Lewis’s woodpecker (Melanerpes lewis)

 

A graphic depicting 3 salvage harvest treatment types and a control designed to benefit each of the target woodpecker species (Dave Halemeier 2016).

Approaches

Analyses will consider pre- and post-salvage variables to determine changes in forest structure alongside woodpecker population dynamics. I would like to learn about spatial autocorrelation analyses such as Moran’s I. It is likely I will find patterns of dependent observations based on localized conditions. Woodpecker species presence and nest locations may be affected by burn severity, since highly weakened trees will host their primary food source, bark beetle larvae. In 2017 woodpecker species presence may be grouped according to salvage treatments targeting each species, or control areas. Regression analyses showing the relationship strength between nest distance from salvage units and salvage treatment types could indicate certain forest variables affecting postfire woodpecker colonization.

Expected Outcome

Regression coefficients describing the relationship between woodpecker presence and salvage treatment location and type will help develop inferences towards postfire management effects. I will create interpretive maps of nest locations showing survey unit and salvage unit polygons. These maps could include statistical and geospatial relationships represented with different colors and symbols. Eventually, I will geovisualize the lidar data with these maps and statistical relationships for a comprehensive and communicative representation of woodpecker population and forest structure change dynamics.

Significance

I am processing two lidar datasets of the study area from 2016 and 2017. These datasets were acquired before and after the salvage logging treatments occurred. I will produce forest metrics such as stand density, diameter class, and height in salvage and survey units. I will then correlate geospatial and statistical properties of the nest dataset to quantified forest variables affecting woodpecker nest site selection. I will examine trends between 2016 and 2017 nest selection to understand the effects of harvest treatments on woodpecker populations. At least two more years of woodpecker data will exist for 2018 and 2019, so future research will add these datasets to the analyses. I would like to see a machine learning algorithm developed from this study that could predict areas of optimal habitat suitability for snag-dependent wildlife species. Postfire wildlife habitat prediction will be crucial to resource managers as wildfires increase in the coming decades alongside accelerated landscape restoration.

This spatial problem is important to science and resource managers as climate change amplifies wildfire effects. Using 3D remote sensing datasets for resource management is the future trend across all disciplines. Increased wildfire activity around the world necessitates cutting-edge methods for active fire and postfire ecosystem quantification. In the Blue Mountains ecoregion in eastern Oregon, Rocky Mountain Research Station, Malheur National Forest, and Blue Mountains Forest Partners rely on this project’s lidar quantification for their research and management decisions. Determining acceptable levels of salvage harvest for wildlife habitat affects whether government agencies and rural economies in this region will allow small businesses to profit from sustainable harvest operations. Science overall will benefit from the continued investigation of wildlife response to anthropogenic disturbance, specifically postfire forest management decisions and the controversial practice of salvage logging.

Above: A salvage treatment in the Crazy Creek woodpecker survey unit.

Preparation

I took an ArcInfo class (ARC Macro Language) during my undergraduate program. I am currently taking a Python class for geospatial programming. I have experience in image processing through an undergraduate degree in land use and GIS, multiple years of professional experience in remote sensing and GIS, and my current MS program in Forest Geomatics. I have academic and professional experience with R, C++, ArcGIS, and multiple types of remote sensing software for 2D and 3D data analysis.

Monitoring Stage 0 Restoration Effects on the South Fork McKenzie River

Research Question

How is the spatial presence of aquatic organisms in the lower South Fork McKenzie River related to the spatial presence of hydromorphological changes induced by Stage 0 stream restoration?

Global hydrologic systems historically contained anastomosing (braided and connected) channels and active floodplains. Anthropogenic disturbances of the late 19th and early 20th centuries and prevailing natural disturbances are responsible for the channelization and incision of stream systems worldwide. Stage 0 riparian restoration restores degraded streams to historic conditions through large woody debris placement and filling in channels to increase aquatic habitat quality and availability. In this study, I will explore Stage 0 restoration effects on a 2 mile reach of the lower South Fork McKenzie River 45 miles east of Eugene, OR. I will test these effects by spatially relating early post-restoration species colonization to restoration-induced hydromorphological changes. These morphological changes include habitat features created by the reduction and redirection of water flow energy, such as riffles, pools, side channels, slack water, and sediment and organic material deposition. Aquatic organisms depend on these features for critical habitat. Therefore, sampling for aquatic species presence before and after restoration will indicate how successful Stage 0 restoration was in creating habitat features for these organisms.

Datasets

I will analyze two biological datasets to detect species presence and multiple remote sensing datasets to detect hydromorphological features. The first biological dataset includes pre- and post-restoration lentic (still freshwater) and lotic (rapid freshwater) aquatic macroinvertebrate samples taken at established transects and randomly throughout the study area. The second biological dataset includes eDNA samples taken at these same transects, intended to capture up to 48 species in a single sample (macroinvertebrates, fish, amphibians, crayfish). The remote sensing datasets include pre- and post-restoration aerial lidar, bathymetric lidar, Structure from Motion, RGB, multispectral, and thermal infrared products acquired with an Unmanned Aerial System for the 2 mile reach. The temporal resolution for all datasets are approximately 1 year (summer 2018 – summer 2019). The datasets are collected pre- and post-implementation (summer 2018) in the early summer during high flow conditions and early fall for low flow conditions. The spatial resolution of the UAS datasets is fine scale (sub-meter to cm).

 Hypotheses

In the biological datasets, I expect to see macroinvertebrate presence clustered around features characterized by low velocity, shallow depth, and high organic material. These features allow macroinvertebrates foraging ease and shelter from predators. I expect species type to be dominated by post-disturbance colonizers throughout the system. I expect the eDNA data to return descriptions of upstream-downstream species presence. I expect non-macroinvertebrate species to be present in areas with more pools, side channels, and high wetted area, which provide habitat for resting, feeding, and nurseries. I expect the macroinvertebrate eDNA data to reflect the physical sampling data. In the remote sensing datasets, I expect habitat features to be present downstream of and adjacent to large woody debris, sediment deposits, and organic material, since these factors reduce and redirect flow energy for establishment of lentic and lotic features (e.g. pools, riffles, slack water).

 Approaches

All analyses will consider pre- and post-restoration variables to determine rate and degree of change. I would like to learn about spatial autocorrelation analyses such as Moran’s I. It is likely that in these datasets I will find patterns of dependent observations based on localized conditions. For example, aquatic species presence may be grouped according to centralized nursery or hatch locations, so they are not truly independent samples. Fish species presence may be grouped based on macroinvertebrate presence as a food source, and certain species may be grouped by areas of similar substrate sizes. Regression analyses showing relationship strengths between different combinations of species and morphology variables (such as PCA) could indicate the likelihood of certain features affecting species colonization.

Expected Outcome

I will produce site maps showing point and polygon locations of certain hydrologic features alongside point locations of specific aquatic species presences and types. For the eDNA datasets, I will produce graphs indicating the presence and type of species detected. Ideally, I will be able to determine correlation coefficients for the relationships between specific hydrologic features created and the presence and location of aquatic species types, likely presenting them in a correlation matrix.

 Significance

 This research presents a novel opportunity to study Stage 0 restoration. Powers et al. (2018) present one of the only formal studies investigating Stage 0 restoration outcomes. This study will add to the limited knowledge surrounding this relatively unexamined strategy. To my knowledge, this proposal will be the first study testing Stage 0 UAS monitoring and one of the few existing studies linking aquatic organism sampling to UAS hydromorphology datasets. On a regional scale, U.S. Forest Service fisheries and hydrology divisions in the Pacific Northwest and across the United States will design restoration effectiveness monitoring objectives using results from this study. The McKenzie Watershed Council will determine their implementation techniques and success rates on future projects with results from this study. The study results will provide a viable Stage 0 restoration monitoring methodology for agencies and landowners on a global scale. The South Fork McKenzie River also sustains fish species listed as Endangered and Threatened under the Endangered Species Act, specifically the Chinook salmon (Onchoryncus tshawytscha) and bull trout (Salvelinus confluentus), respectively (USFWS 2019). These species use the South Fork McKenzie River for annual spawning and rearing habitat and feed on resident macroinvertebrates (Meyer et al. 2016). The Chinook salmon and bull trout are among the western U.S.’s most controversial game fish due to the environmental policies surrounding their protection. Conflicts among agencies, companies, and the public frequently arise concerning these species’ conservation. Researching restoration effects on Chinook salmon and bull trout habitat and food sources will provide scientific basis for management decisions regarding these fish.

Level of Preparation

I took an Arc-Info (ARC Macro Language) class during my undergraduate program. I am currently taking a Python class for geospatial programming, which is my only experience with this language. I have experience in image processing through an undergraduate degree in land use and GIS, multiple years of professional experience in remote sensing and GIS, and my current MS program in Forest Geomatics. I have academic and professional experience with R, C++, ArcGIS, and multiple types of remote sensing software for 2D and 3D analysis.

References

Meyer, K., Hammons, B., Hogervorst, J., Powers, P., Weybright, J., Bair, B., Robertson, G., Mazullo, C. (2016). Lower South Fork McKenzie River Floodplain Enhancement Project 80% Design Report. Prepared by the Willammette National Forest and McKenzie Watershed Council, March 4, 2016.

Meyer K. (2018). Deer Creek: Stage 0 alluvial valley restoration in the western Cascades of Oregon. In StreamNotes: The Technical Newsletter of the National Stream and Aquatic Ecology Center, David Levinson (editor). US FOrest Service, Fort Collins, CO, May 2018. https://www.fs.fed.us/biology/nsaec/assets/streamnotes2018‐05.pdf

Newson, M.D., Newson, C.L. (2000). Geomorphology, ecology and river channel habitat: mesoscale approaches to basin-scale challenges. Progress in Physical Geography 24(2), 195 – 217.

Powers, P. D., Helstab, M., & Niezgoda, S. L. (2019). A process-based approach to restoring depositional river valleys to Stage 0, an anastomosing channel network. River Research and Applications, 35(1), 3–13. https://doi.org/10.1002/rra.3378

U.S. Fish and Wildlife Service. (2019). Environmental Conservation Online System. https://www.fws.gov/endangered/