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.

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

                           

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.

 

 

 

 

 

 

 

 

 

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