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.

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.

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.