Nesting success of scissor-tailed flycatchers (Tyrannus forficatus) at a wind farm in northern Texas.
Recent research has focused on indirect impacts of wind turbines on wildlife, such as loss of habitat, displacement, and behavioral changes in response to wind turbines (Drewitt and Langston, 2006; Kuvlesky et al., 2007). Construction of wind facilities requires less loss of habitat than other energy-extraction industries such as drilling for oil and gas or mining coal (Kuvlesky et al., 2007). The typical modern wind turbine has a footprint (land impacted directly) of 0.08-0.20 ha and, collectively, the footprint comprises only 2-5% of the total impact of a wind facility (Fox et al., 2006). Fragmentation, displacement, and avoidance may collectively have greater impacts than the small amount of habitat lost from construction of these wind facilities. Additional roads are of greatest concern because they further fragment habitat and create a network of disturbed areas that can facilitate colonization of invasive plants (Gelbard and Belnap, 2003; Rentch et al., 2005). Displacement can magnify the amount of lost habitat if birds avoid areas near wind turbines because of visual, auditory, or vibration impacts from the wind turbines themselves or because of disturbance from vehicles or personnel related to maintenance of wind facilities (Drewitt and Langston, 2006). Leddy et al. (1999) reported that densities of grassland passerines within 180 m of wind turbines were less than comparable habitat without turbines. Lesser prairie chickens (Tympanuchus pallidicinctus) and greater prairie chickens (Tympanuchus cupido) avoided powerlines and lesser prairie chickens avoided a highway within otherwise suitable habitat (Pruett et al., 2009). Wind turbines also can displace birds during migration (e.g., Desholm and Kahlert, 2005). Radar-tracking of migratory European birds revealed that migrants adjusted their course to avoid off-shore wind facilities (T. K. Christensen et al., in litt.; J. Kahlert et al., in litt.). Most migrants can avoid one wind facility easily, but multiple wind facilities on the migration path could cause an increase in failed migrations (Masden et al., 2009). Migrants could attempt to use off-shore turbines as a rest stop, which would increase chances of collisions with rotors (Desholm and Kahlert, 2005).
Development of wind facilities has more than doubled in recent years (Tollefson, 2008). Texas had an installed wind capacity of 1,290 MW in 2004, which increased to 9,708 MW by July 2010 (American Wind Energy Association, in litt.); three times greater than the next highest state (Iowa). The first wind facilities in Texas were built in areas with highest mean wind speeds, but new wind facilities are being built further east and north using newer technologies to exploit the lower, but economically viable, mean wind speeds (S. Combs, in litt.). Thus, wind facilities in Texas are now widespread and numerous. Not only could development of wind energy potentially impact grasslands birds in Texas, but these areas also coincide with an important migratory pathway, the Central Flyway (Brown et al., 2001). Operations of wind turbines represent a potential new source of pressure on populations of birds. This could be particularly true for migratory species where Texas comprises nearly all of their breeding range.
The purpose of our study was to investigate the potential, indirect effects of wind turbines on a migratory grassland bird, the scissor-tailed flycatcher Tyrannus forficatus, with the majority of its summer breeding range in Texas. Because wind energy has undergone recent and massive growth in Texas, we were interested in determining whether alteration of landscape from construction of wind facilities impacted a relatively unexplored facet of indirect effects: how do wind turbines impact nesting success? We sought to identify environmental variables, in addition to proximity to wind turbines, which best explain survival of nests in breeding scissor-tailed flycatchers at an operational wind farm.
MATERIALS AND METHODS--Scissor-tailed flycatchers are a tyrannid flycatcher that over-winters in Central America and migrates north to breed, mostly in Texas and Oklahoma (Regosin, 1998). The scissor-tailed flycatcher generally is common in anthropogenically disturbed areas, has not shown evidence of decline, and is listed as a species of Least Concern by the International Union for the Conservation of Nature and Natural Resources (http://www.iucnredlist.org). During the breeding season, densities are 1.6-3.3 breeding pairs/10 ha, and territories of males are ca. 0.2-0.4 ha in size (Fitch, 1950; Regosin and Pruett-Jones, 1995). Scissor-tailed flycatchers most often construct nests 1.5-12.2 m above ground in isolated trees or along edges of patchy woodlands (Nolte and Fulbright, 1996).
Our study site was Wolf Ridge Wind, LLC, Cooke County, Texas (33[degrees]43'N, 97[degrees]25'W), ca. 20 km S Texas-Oklahoma border. Wolf Ridge, owned and operated by NextEra Energy Resources, began operations in October 2008 and is in the crosstimbers ecoregion of north-central Texas (Engle et al., 2006). Our research was conducted during the first breeding season after the facility became operational. The area consists of woodlands and open grasslands used primarily for grazing cattle and agricultural fields. This patchwork habitat provides suitable breeding and foraging habitats for scissor-tailed flycatchers, which prefer to nest in savannah-like habitat, as well as in agricultural fields and pastures, and to forage from perching spots overlooking open grassy areas (Regosin, 1998). Hackberry (Celtis occidentalis) was the most common tree at the study site, but Ashe juniper (Juniperus ashei) and honey mesquite (Prosopis glandulosa) also were abundant. Little bluestem (Schizachyrium scoparium) and indiangrass (Sorghastrum nutans) were common in hay fields and in some pastures used for grazing livestock. Agricultural fields were either fallow or contained winter wheat.
We observed nesting activity of scissor-tailed flycatchers 25 May-2 August 2009. We searched for nests within 1,592 ha of open fields intersected by 46.7 km offences. We began searches at 0630 h; checking trees for nests and finding individuals or pairs of scissor-tailed flycatchers. We recorded evidence of breeding behavior and followed birds to nests. Although scissor-tailed flycatchers are most active in building nests, defense of territories, and foraging during morning (Regosin and Pruett-Jones, 1995), searches for nests in afternoon also were productive. We used alarm calls elicited by our approach or presence of breeding pairs to locate trees with active nests.
We recorded locations of nests using a handheld GPS unit (Trimble GeoXH; Trimble, Sunnyvale, California). We recorded stage of nesting (under construction, egg laying, incubation, or nestling) and observed parental activity. We used an extendable pole with an attached mirror to look into nests >2 m high. We were able to use the mirrored pole to survey contents of every nest located, except for two nests. One was >13 m high in a tree and the other was directly adjacent to high-powered electrical lines. We revisited each monitored nest at least every 3 days to record status until failure or fledging.
We calculated daily rate of survival of nests using the Mayfield method (Mayfield, 1975). Because not every nest was observed from initiation of construction to fledging, observed rate of mortality is lower than true rate of mortality. We used nest-days as the sampling unit to calculate daily rates of mortality and survival to reduce this underestimation. We calculated probability of a nest surviving from initiation to end of fledging by raising daily rate of survival to the power equal to duration of incubation in days plus duration of nestling in days (Mayfield, 1975).
We collected data on characteristics of habitats associated with nests after each nest either fledged young or failed. For each nest, we recorded height, distance from trunk, height of tree, diameter of tree at breast height (DBH), and percentage of canopy cover. Height was measured to the nearest 0.1 m from the ground to the bottom of the nest. Distance from trunk was from the nearest edge of the nest to the trunk to the nearest 0.1 m. We measured height of tree to the nearest 0.1 m using a telescoping pole and measuring tape, DBH to the nearest 0.1 m was measured ca. 1.5 m from the ground, and canopy coverage (percentage of sky obscured by foliage) was measured using a sighting tube from underneath the nest. We also determined number of woody plants, percentage of ground cover, and height of tree canopy within a circular 0.05-ha plot centered on the nest tree (James, 1992). Inside the plot, we counted woody plants (other than the nest tree) >30 cm in height and recorded species and height of the tallest tree, hereafter referred to as canopy tree. We recorded percentage of vegetative ground cover using a randomly placed 0.5 by 0.5-m quadrat (Bibby et al., 2000).
We used an information-theoretic analysis to concurrently evaluate performance of multiple a priori models (single and multivariate hypotheses) that could potentially affect nest-day failure (Anderson et al., 2000; Burnham and Anderson, 2002; Hazler, 2004). Because the dependent variable was binary (success or failure of nest), we used Mayfield logistic-regression models (modified from the Mayfield method; Mayfield 1961) as outlined by Hazler (2004). To determine which model(s) best explained the binary outcome, we used an information-theoretic approach focusing on strength of evidence provided by a set ofa priori alternative hypotheses rather than a statistical test of null hypotheses against an alpha level (Anderson et al., 2000; Burnham and Anderson, 2002). The Akaike Information Criterion (AIC) provides the best measure of performance of the model in observational data (Burnham and Anderson, 2002); the model with the lowest AIC is the most informative hypothesis. We used the second-order criterion ([AIC.sub.c]), which uses a bias-corrected term for smaller samples. For each model, we also defined number of estimable parameters (K), difference in [AIC.sub.c] between the model of interest and the [AIC.sub.c] of the best model ([DELTA][AIC.sub.c]), and Akaike weight of the model ([omega]). The [DELTA][AIC.sub.c] allowed direct comparison of models relative to the optimum; all models with [DELTA][AIC.sub.c] <2 were considered strong support. Akaike weight gives the relative weight each hypothesis carries in the overall explanation of the dependent variable. We also analyzed relative influence of each individual explanatory variable on the dependent variable by calculating relative importance weight, [[omega].sub.+](i), for each (Burnham and Anderson, 2002). These weights provided additional insight as to variables that best explained the outcome, especially where several models gave overall support. Relative importance weights were calculated by summing Akaike weights, [omega], of each model in which the variable occurred; thus, allowing direct comparison of relative influence of each variable.
We performed information-theoretic analyses using SAS, version 9.1 (SAS Institute, Inc., Cary, North Carolina). Mayfield logistic regression uses nest-exposure days rather than nests as the sampling unit and this could lead to pseudoreplication (Hurlbert, 1984). This potential concern can be identified by conducting goodness-of-fit tests (Hosmer and Lemeshow, 2000); if necessary, an overdispersion correction can be applied to alleviate this concern (Hazler, 2004). We used PROC LOGISTIC using the EVENTS/TRIALS model with the LACKFIT option to test for overdispersion of data (Hazler, 2004) of all possible models; they all passed goodness-of-fit tests and did not require transformations for overdispersion of data. In some instances, candidate models may produce biologically unrealistic estimates (Compton et al., 2002) and, therefore, not achieve the goal of being a useful predictor of reliable estimates of parameters. The statistical software explicitly identified unstable parameters by their failure to converge and we retained only the most robust (i.e., stable) models for interpretation. We performed all spatial analyses with ArcMap Version 9.3.1 (Environmental Systems Research Institute, Redlands, California). Unless otherwise noted, all means are presented [+ or -] 1 SE.
RESULTS--On 26 May 2009, we found our first scissor-tailed flycatcher nest under construction. We found the first eggs on 29 May 2009. On 2 August 2009, the last active nest failed because of predation. First observations of all successful nests were 26 May-8 June 2009.
We found 32 nests, resulting in a density of 0.19 nests/ 10 ha. Five of those nests (16%) fledged [greater than or equal to]1 chick. We discovered 13 nests before the female started laying eggs, six nests when the female was in the process oflaying her clutch, and eight nests already with full clutches. Mean number of chicks fledged from successful nests was 3.8 [+ or -] 0.2. Mean size of clutch was 4.2 [+ or -] 0.3 eggs. All unsuccessful nests failed due to predation except one, which failed due to abandonment of two intact eggs. Overall daily rate of survival for nests at Wolf Ridge was 0.94 [+ or -] 0.13 during 413 nest-days. Nests at Wolf Ridge had a 13.3% chance of producing >1 fledgling.
Nests of scissor-tailed flycatchers were over a wide range of distances from wind turbines. About two-thirds of nests were within 125-407 m of the nearest wind turbine (n = 32, mean = 257 [+ or -] 25 m, range = 52-573 m).
Of 32 nests, 28 were built in canopies of trees in each 0.05-ha plot. Scissor-tailed flycatchers readily used anthropogenic structures for nesting. Artificial structures (e.g., utility poles) were considered a canopy or nest tree if they contained a nest. Four nests were behind transformers on utility poles and all of these successfully fledged young. Along fences, we located 19 nests in trees, whereas 9 were in isolated trees in open fields. Most nests were on the north (315-45[degrees]; n = 11) or west (225-315[degrees]; n = 10) side of the tree. Only five nests were on the east side (45-135[degrees]) and five on the south (135-225[degrees]). One nest was in the center fork of the trunk.
Four nests with predation by red imported fire ants (Solenopsis invicta) were, on average, closer to the ground than other unsuccessful nests (mean height of nests with predation by red imported fire ants = 2.10 [+ or -] 0.28 m, n = 4; mean height of other unsuccessful nests = 3.40 [+ or -] 0.35 m, n = 22; Mann-Whitney U = 27.0, P = 0.06), but not significantly closer to the ground. Red imported fire ants consumed eggs by chewing a small hole in the shell to reach the contents, effectively leaving eggshells empty but intact. These ants also consumed nestlings; predation by red imported fire ants was confirmed by observing ants and remains of nestlings in the nest.
Using an information-theoretic approach, we tested 63 models, all of which were considered stable and passed tests for pseudoreplication. The hypothesis of distance to turbine and percentage of canopy coverage combined was the model that best explained nesting success of scissor-tailed flycatchers at Wolf Ridge ([DELTA][AIC.sub.c] = 204.06, [omega] = 0.149; Table 1), although four other hypotheses also had strong support ([DELTA][AIC.sub.c] <2; Table 1). The variable most associated with nesting success was percentage of canopy cover ([[omega].sub.+](i) = 0.94). Successful nests had an average 12% canopy cover (n = 5, SE = 12.00) versus 66.9% (n = 27, SE = 4.98) in failed nests. The second strongest variable was distance to turbine ([[omega].sub.+](i) = 0.79). Successful nests were closer to turbines (210 [+ or -] 48 m, n = 5) and failed nests were farther from turbines (269 [+ or -] 28 m, n = 27). Height of canopy ([[omega].sub.+](i) = 0.41) and DBH ([[omega].sub.+](i) = 0.45) of nest tree were the next most influential variables (larger for successful nests than for failed nests), but these were generally less effective in explaining the observed pattern of nesting success than either percentage of canopy cover or distance to turbine. Least influential variables were height of nest ([[omega].sub.+](i) = 0.35) and distance from trunk ([[omega].sub.+](i) = 0.32).
DISCUSSION--Nesting success in scissor-tailed flycatchers was best explained by distance to nearest wind turbine in conjunction with percentage of canopy cover. Scissor-tailed flycatchers nesting closer to turbines and with less canopy cover over nests had the highest nesting success. Overall reproductive success was low at the wind farm (daily rate of survival = 0.935) in 2009. Our estimations of nesting success were lower than estimated daily rate of survival of 0.994 for scissor-tailed flycatchers in Brazos County, Texas (Fitch, 1950). Assuming a 26.5-day period from start of incubation to fledgling (12.5 day incubation, 14 day nestling stage; Baicich and Harrison, 2005), each nest in Brazos County would have had an 85.3% chance of fledging young. This is higher than the 13.3% chance of fledging young at Wolf Ridge. Even with substantial interannual variation, other studies also have reported higher nesting success in scissor-tailed flycatchers. For example, 76.7% of nests in 1991 and 38.7% of nests in 1992 successfully fledged young at Fort Sill Military Reservation in Comanche County, Oklahoma (Regosin and Pruett-Jones, 1995). The study site in Oklahoma was smaller (800 ha) than our study site and was a mesquite (Prosopis juliflora) savannah with 100 ha of mowed grass and planted trees. Regosin and Pruett-Jones (1995) also reported a greater percentage of nests successfully fledging young than we observed. Although study sites of Fitch (1950) and Regosin and Pruett-Jones (1995) were in different regions than ours, all studies showed scissor-tailed flycatchers breeding in habitats influenced by anthropogenic activities.
Most scissor-tailed flycatchers nested in trees along fences at Wolf Ridge. Grazing and agricultural fields contained few isolated nest trees and scissor-tailed flycatchers rarely nested along the edge of large woodland areas. It is likely that scissor-tailed flycatchers avoided wooded areas because the canopies were too crowded. Nolte and Fulbright (1996) also noted that successful nests tended to have less surrounding cover than failed nests. While fences provided trees with sufficently open canopies to attract scissor-tailed flycatchers, they also could be corridors for mammalian and reptilian predators (Winter et al., 2000; Klug et al., 2010) such as raccoons (Procyon lotor), Virginia opossums (Didelphis virginiana), striped skunks (Mephitis mephitis), and western rat snakes (Elaphe obsoleta). We observed these types of predators during our searches and monitoring. It is likely that both prey and predators are channeled to the same relatively small areas and corridors at Wolf Ridge, which is similar to conclusions reached by Donovan et al. (1997) and Bader and Bednarz (2009).
Because scissor-tailed flycatchers are neither colonial nesters nor builders of cryptic nests, a good nesting site might have placement that limits access by predators (Nolte and Fulbright, 1996). Although nests built away from fence corridors may be more exposed to aerial predators, overall predation pressure exerted by terrestrial predators may be greater. Four of the five successful nests at Wolf Ridge had no canopy cover and breeding adults aggressively defended nests by vocalizing loudly and harassing black vultures (Coragyps atratus)and passing aerial predators (e.g., hawks). These four nests were on utility poles. It is probable that some, if not most, terrestrial and arboreal predators were unable to climb the relatively smooth and vertical surface. Mullin and Cooper (2002) tested climbing abilities of western rat snakes and reported rates of failure was greatest in snakes climbing Nuttall oak trees (Quercus texana), which have a smoother bark compared to overcup oak (Q. lyrata) and sugarberry (C. laevigata) trees, which have large irregular plates or ridges with fissures between plates and corky warts or ridges punctuating otherwise smooth bark, respectively. In 6 of 27 failed nests, the entire clutch disappeared without any evidence of broken eggs nearby suggesting predation by snakes (Best, 1978). The other 21 nests had evidence of depredation by other predators; either the nest was damaged and eggs were broken inside the nest or on the ground below, or discarded egg shells and dead nestlings remained inside the nest.
In our study, there was no significant adverse effect of wind turbines on fate of nests of scissor-tailed flycatchers. Instead, habitat associated with possible reductions in predation appear to best explain patterns of nesting success. Successful nests were in locations with less canopy coverage and closer to turbines than failed nests. Because predation by snakes was common, less canopy coverage might inhibit ability of arboreal snakes to readily access nests of scissor-tailed flycatchers. Reduced canopy coverage also could reduce predation by other terrestrial vertebrates, especially if reduced canopy coverage is associated with exclusion from tree corridors (where predators are likely to move through the area). However, less canopy coverage might also make the nest more vulnerable to predation from aerial predators. To counteract this, scissor-tailed flycatchers nesting closer to turbines might benefit from reduced activity of raptors near turbines. For invertebrate predators, such as red imported fire ants, height of nest could be important. Nests infested by red imported fire ants typically were lower to the ground than those not infested by ants.
Funding was provided by the Texas Christian University-Oxford University-NextEra Energy Resources Wind Research Initiative. NextEra Energy Resources provided unrestricted access to data. We thank Wolf Ridge Wind, LLC, for logistical support; T. Trowbridge and C. Page were especially helpful. Additional funding and support was provided by the Institute for Environmental Studies and the Department of Biology at Texas Christian University. We thank M. Slattery, D. Williams, S. Eady, and K. Ozenick for comments on an early version of the manuscript, S. F. Fox for help with the Spanish resumen, and R. Perry, A. Medina, J. Bull, M. Jantzen, W. Martin, B. Suson, A. Lapine, J. Ellis, and T. Stevens for work during collection of data.
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Submitted 1 November 2010.
Accepted 21 February 2012.
Guest Associate Editor was John T. Baccus.
TREVOR G. RUBENSTAHL, AMANDA M. HALE, * AND KRISTOPHER B. KARSTEN
Institute for Environmental Studies, Texas Christian University, Fort Worth, TX 76129 (TGR, KBK)
Department of Biology, Texas Christian University, Fort Worth, TX 76129 (AMH, KBK)
* Correspondent: firstname.lastname@example.org
TABLE 1--Mayfield logistic-regression analysis of habitat variables on nesting success of scissor-tailed flycatchers (Tyrannus forficatus) at Wolf Ridge, Cooke County, Texas, 2009. Only the first 10 of 63 models are presented. Log-likelihood Model function (Log L) Percentage of canopy cover + distance to nearest wind turbine -99.004 Height of nest + percentage of canopy cover + distance to nearest wind turbine -98.611 Diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine -97.667 Diameter at breast height + percentage of canopy cover + distance to nearest wind turbine -98.730 Distance from trunk + percentage of canopy cover + distance to nearest wind turbine -98.874 Height of canopy + percentage of canopy cover + distance to nearest wind turbine -98.992 Height of nest + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine -97.375 Distance from trunk + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine -97.453 Diameter at breast height + height of canopy + percentage of canopy cover -99.455 Height of nest + height of canopy + percentage of canopy cover + distance to nearest wind turbine -98.486 Number of estimated Model parameters (K) Percentage of canopy cover + distance to nearest wind turbine 3 Height of nest + percentage of canopy cover + distance to nearest wind turbine 4 Diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 5 Diameter at breast height + percentage of canopy cover + distance to nearest wind turbine 4 Distance from trunk + percentage of canopy cover + distance to nearest wind turbine 4 Height of canopy + percentage of canopy cover + distance to nearest wind turbine 4 Height of nest + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 6 Distance from trunk + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 6 Diameter at breast height + height of canopy + percentage of canopy cover 4 Height of nest + height of canopy + percentage of canopy cover + distance to nearest wind turbine 5 Selection criterion Model ([AIC.sub.c]) Percentage of canopy cover + distance to nearest wind turbine 204.06 Height of nest + percentage of canopy cover + distance to nearest wind turbine 205.30 Diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 205.45 Diameter at breast height + percentage of canopy cover + distance to nearest wind turbine 205.54 Distance from trunk + percentage of canopy cover + distance to nearest wind turbine 205.83 Height of canopy + percentage of canopy cover + distance to nearest wind turbine 206.06 Height of nest + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 206.91 Distance from trunk + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 207.06 Diameter at breast height + height of canopy + percentage of canopy cover 206.99 Height of nest + height of canopy + percentage of canopy cover + distance to nearest wind turbine 207.09 Simple differences Model ([AAIC.sub.c]) Percentage of canopy cover + distance to nearest wind turbine 0.00 Height of nest + percentage of canopy cover + distance to nearest wind turbine 1.24 Diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 1.39 Diameter at breast height + percentage of canopy cover + distance to nearest wind turbine 1.48 Distance from trunk + percentage of canopy cover + distance to nearest wind turbine 1.77 Height of canopy + percentage of canopy cover + distance to nearest wind turbine 2.01 Height of nest + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 2.85 Distance from trunk + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 3.01 Diameter at breast height + height of canopy + percentage of canopy cover 2.93 Height of nest + height of canopy + percentage of canopy cover + distance to nearest wind turbine 3.03 Akaike Model weights ([omega]) Percentage of canopy cover + distance to nearest wind turbine 0.149 Height of nest + percentage of canopy cover + distance to nearest wind turbine 0.080 Diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 0.074 Diameter at breast height + percentage of canopy cover + distance to nearest wind turbine 0.071 Distance from trunk + percentage of canopy cover + distance to nearest wind turbine 0.062 Height of canopy + percentage of canopy cover + distance to nearest wind turbine 0.055 Height of nest + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 0.036 Distance from trunk + diameter at breast height + height of canopy + percentage of canopy cover + distance to nearest wind turbine 0.033 Diameter at breast height + height of canopy + percentage of canopy cover 0.034 Height of nest + height of canopy + percentage of canopy cover + distance to nearest wind turbine 0.033
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|Author:||Rubenstahl, Trevor G.; Hale, Amanda M.; Karsten, Kristopher B.|
|Date:||Jun 1, 2012|
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