DOE OSTI2013
The consistent wind resource in the Great Plains of North America has encouraged the development of wind energy facilities across this region. In the Texas Panhandle, a high quality wind resource is only one factor that has led to the expansion of wind energy development. Other factors include federal tax incentives and the availability of subsidies. Moreover, the State Renewable Portfolio Standards (RPS), mandating production of 10,000 mega-watts of renewable energy in the state by 2025, has contributed to an amicable regulatory and permitting environment (State Energy Conservation Office 2010). Considering the current rate of development, the RPS will be met in coming years (American Wind Energy Association 2011) and the rate of development is likely to continue. To meet increased energy demands in the face of a chronically constrained transmission grid, Texas has developed a comprehensive plan that organizes and prioritizes new transmission systems in high quality wind resource areas called Competitive Renewable Energy Zones (CREZ). The CREZ plan provides developers a solution to transmission constraints and unlocks large areas of undeveloped wind resource areas. In the northern Texas panhandle, there are two CREZs that are classified Class 3 wind (Class 5 is the highest) and range from 862,725 to 1,772,328 ha in size (Public Utility Commission of Texas 2008). Grassland bird populations have declined more than any other bird group in North America (Peterjohn and Sauer 1999, Sauer et al. 2004). Loss of grassland habitat from agricultural development has been the greatest contributor to the decline of grassland bird populations, but development of non-renewable (i.e., oil, coal, and gas) and renewable energy (i.e., wind, solar, biomass, and geothermal) sources have contributed to the decline as well (Pimentel et al. 2002, Maybe and Paul 2007). The effects of wind energy development on declining grassland bird populations has become an area of extensive research, as we attempt to understand and minimize potential impacts of a growing energy sector on declining bird populations. Based on data from post-construction fatality surveys, two grassland bird groups have been the specific focus of research, passerines (songbird guild) and raptors (birds of prey). The effects of wind energy development on these two groups of birds, both of conservation concern, have been examined over the last decade. The primary focus of this research has been on mortality resulting from collision with wind turbines (Kuvlesky et al. 2007). Most studies just quantify post-construction fatality levels (e.g., Erickson et al. 2002) while very few studies provide a comparison of bird populations prior to development through a Before-After-Control-Impact (BACI) study design. Before-After-Control-Impact studies provide powerful evidence of avian/wind energy relationships (Anderson et al. 1999). Despite repeated urgency on conducting these types of studies (Anderson et al. 1999, Madders and Whitfield 2006, Kuvelsky et al. 2007), few have been conducted in North America. Although several European researchers (Larsson 1994, de Lucas et al. 2007) have used BACI designs to examine whether wind facilities modified raptor behavior, there is a scarcity of BACI data relating to North America grassland ecosystems that examine avian-wind energy relationships. There are less than a handful of studies in the entire United States, let alone the southern short grass prairie ecosystem, that incorporate preconstruction data to form the baseline for post-construction impact estimates (Johnson et al. 2000, Erickson et al. 2002). Although declines in grassland bird populations are well-documented (Peterjohn and Sauer 1999, Sauer et al. 2004), the causal mechanisms affecting the decline of grassland birds with increasing wind energy development in the southern short grass prairie are not well-understood (Kuvlesky et al. 2007, Maybe and Paul 2007). Several factors may potentially affect the bird population when wind turbines are constructed in areas with high bird densities (de Lucas et al. 2007). Habitat fragmentation, noise from turbines, physical movement of turbine blades, and increased vehicle traffic have been suggested as causes of decreased density of nesting grassland birds in Minnesota (Leddy et al. 1999), Oklahoma (O’Connell and Piorkowski 2006), and South Dakota (Shaffer and Johnson 2008). Similarly, constructing turbines in areas where bird flight patterns place them at similar heights of turbine blades increases the potential for bird collisions (Johnson et al. 2000, Hoover 2002). Raptor fatalities have been associated with topographic features such as ridges, saddles and rims where birds use updrafts from prevailing winds (Erickson et al. 2000, Johnson et al. 2000, Barrios and Rodriquez 2004, Hoover and Morrison 2005). Thus, wind energy development can result in indirect (e.g., habitat avoidance, decreased nest success) and direct (e.g., collision fatalities) impacts to bird populations (Anderson et al. 1999). Directly quantifying the level of potential impacts (e.g., estimated fatalities/mega-watthour) from wind energy development is beyond the scope of this study. Instead, I aim to quantify density, occupancy and flight behavior for the two bird groups mentioned earlier: obligate grassland songbirds and raptors, respectively, predict where impacts may occur, and provide management recommendations to minimize potential impacts. The United States Department of Energy (DOE), through the Office of Energy Efficiency and Renewable Energy Allocation, contracted Texas Tech University to investigate grassland bird patterns of occurrence in the anticipated CREZ in support of DOE’s 20% Wind Energy by 2030 initiative. In cooperation with Iberdrola Renewables, Inc., studies initiated by Wulff (2010) at Texas Tech University were continued at an area proposed for wind energy development and a separate reference site unassociated with wind energy development. I focused on four primary objectives and this thesis is accordingly organized in four separate chapters that address grassland bird density, grassland bird occupancy, raptor flight patterns, and finally I summarize species diversity and composition. The following chapters use formatting from the Journal of Wildlife Management guidelines (Block et al. 2011) with modifications as required by the Texas Tech University Graduate School. 1) I estimate pre-construction bird density patterns using methods that adjust for imperfect detection. I used a distance sampling protocol that effectively accounts for incomplete detection in the field where birds are present but not detected (Buckland et al. 2001). I improved density estimates with hierarchical distance sampling models, a modeling technique that effectively incorporates the detection process with environmental covariates that further influence bird density (Royle et al. 2004, Royle and Dorazio 2008). Covariates included road density and current oil and gas infrastructure to determine the relationship between existing energy development and bird density patterns. Further, I used remote sensing techniques and vegetation field data to investigate how landcover characteristics influenced bird density patterns. I focused species-specific analyses on obligate grassland birds with >70 detections per season namely grasshopper sparrow (Ammodramus savannarum) and horned lark (Eremophila alpestris). Chapter II focuses on hierarchical models that model and describe relationships between grassland bird density and anthropogenic and landscape features. 2) A large number of bird detections (>70) are needed to estimate density using distance sampling and collection of such quantity are often not feasible, particularly for cryptic species or species that naturally occur at low densities (Buckland et al. 2001). Occupancy models operate with far fewer data and are often used as a surrogate for bird abundance when there are fewer detections (MacKenzie and Nichols 2004). I used occupancy models that allow for the possibility of imperfect detection and species abundance to improve estimates of occurrence probability (Royal 2004). I focused species-specific analyses on grassland birds with few detections: Cassin’s sparrow (Peucaea cassinii), eastern meadowlark (Sturnella magna), and upland sandpiper (Bartramia longicauda). Chapter III uses a multi-season dynamic site occupancy model that incorporates bird abundance to better estimate occurrence probability. 3) When I considered the topographic relief of the study sites, the proposed design of the wind facility and its location within the central U.S. migratory corridor, I expanded the study to investigate raptor abundance and flight behavior (Hoover 2002, Miller 2008). I developed a new survey technique that improved the accuracy of raptor flight height estimates and compared seasonal counts and flight heights at the plateau rim and areas further inland. I used counts and flight behaviors to calculate species-specific collision risk indices for raptors based on topographic features. I focused species-specific analyses on raptors with the highest counts: American kestrel (Falco sparverius), northern harrier (Circus cyaneus), red-tailed hawk (Buteo jamaicensis), Swainson’s hawk (Buteo swainsoni), and turkey vulture (Cathartes aura). Chapter IV describes patterns of seasonal raptor abundance and flight behavior and how topography modulates collision risk with proposed wind energy turbines. 4) Finally, for completeness, in Chapter V I summarize morning point count data for all species and provide estimates of relative composition and species diversity with the Shannon-Wiener Diversity Index (Shannon and Weaver 1949).