# Map-Based Repowering and Reorganization of a Wind Resource Area to Minimize Burrowing Owl and Other Bird Fatalities

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## Abstract

**:**

^{2}grid cell among 187,908 grid cells sampled from 2,281,169 grid cells comprising a digital elevation model (DEM) of the study area. Fuzzy logic and discriminant function analysis produced likelihood surfaces encompassing most burrowing owl burrows within a fraction of the study area, and the former corresponded with burrowing owl fatalities and the latter with other raptor fatalities. Our ratings of wind turbine hazard were more predictive of burrowing owl fatalities, but would be more difficult to implement. Careful repowering to modern wind turbines would most reduce fatalities of burrowing owls and other birds while adding about 1,000 GWh annually toward California’s 33% Renewable Portfolio Standard.

## 1. Introduction

## 2. Study Area

## 3. Methods

#### 3.1. Estimating Fatality Rates

**Figure 1.**Some of the various old-generation wind turbine models in the APWRA during 1998-2007, including Nordtank 65-kW (A), KCS56 100-kW (B), Bonus 150-kW (C), Polenko 100-kW (D), Windmatic 65-kW (E), Howden 330-kW (F), Micon 65-kW (G), Enertech 40-kW (H), Flowind 150-kW (I), and KVS-33 400-kW (J). These photos are not to scale.

**Figure 2.**Vestas 660-kW turbines in Diablo Winds Energy Project, which replaced Flowind vertical axis turbines (left photo), and Mitsubishi 1-MW turbines in Buena Vista Wind Energy project that replaced Windmaster, Nordtank, and Danwin turbines (right photo).

_{A}, for carcasses not found due to searcher detection error and scavenger removal [2]:

_{U}was unadjusted fatality rate, p was the average proportion of fatalities found by searchers during searcher detection trials across the U.S. [15], and R

_{C}was the average cumulative proportion of carcasses remaining since the last fatality search, assuming wind turbines deposit carcasses steadily through the search interval. Preliminary R

_{C}values were estimated using reports of scavenger removal trials across the U.S. [15]:

_{i}was the proportion of carcasses remaining by the ith day into a scavenger removal trial and corresponding with days since the last search during fatality monitoring, and I was the average search interval (days). We looked up R

_{C}values in [15, App.], but we note that new approaches to scavenger removal trials have been generating faster removal rates (Smallwood et al., manuscript in preparation). Searcher detection and scavenger removal rates varied across the U.S., but not nearly to the degree they varied by typical body size categories and by whether raptors or nonraptors [15]; nevertheless, multiple sources of error and bias have yet to be characterized.

#### 3.2. Mapping Burrowing Owl and Mammal Burrows for Model Development

#### 3.3. Spatial Models to Predict Burrowing Owl Locations

^{2}cells. The analytical grid was used to develop and test predictive models, which were later projected across the 2,281,169 grid cells composing the APWRA. The analytical grid was not selected randomly from within the APWRA because the focus of the burrow mapping was on raptor prey species nearby the wind turbines, most of which were placed along ridge crests and ridgelines between peaks and valley bottoms. Thus, some landscape features within the analytical grid were disproportional to their occurrence within the APWRA, such as ridge crests. Model predictions will be more reliable for landscape features represented within the analytical grid than for landscape features typically farther away from wind turbines.

**Figure 3.**Ridge and valley features expressed as blue and gold, respectively, and typical of convex-trending groups of DEM grid cells (ridges) and concave-trending groups of grid cells (valleys).

**Figure 4.**Line coverages of ridge tops (orange) and valley bottoms (blue) following multiple geoprocessing steps assessing trends in neighboring DEM grid cells. Polygons enclose areas around wind turbines where burrow systems of ground squirrel (green) and burrowing owls (magenta) were mapped to develop predictive models.

**Figure 5.**Example depiction of how slope attributes were measured from 10 m

^{2}DEM grid cells. The elevation difference was the Elevation to valley + Elevation to ridge, and elevation ratio was Elevation to valley ÷ Elevation to ridge. Total slope distance was Distance to valley + Distance to ridge, and distance ratio was Distance to valley ÷ Distance to ridge. Gross slope was elevation difference ÷ total slope distance. The hypothetical grid cell overlaps a burrowing owl burrow located on another project site in the APWRA.

_{10}and natural log transformations were used to better fit normal distributions, and then chi-square tests for association and principal components analysis (PCA) were used to further understand how the variables related to burrowing owl burrow locations and to each other. To minimize the effects of confounding, no more than one predictor variable was selected from each principle component for any model developed to classify grid cells according to whether they supported burrowing owl burrows. The first modeling approach used discriminant function analysis (DFA), and the second used fuzzy logic [19,20]. Both produced likelihood surface areas, one referred to as the DFA surface and the other as FL surface. The performance of each model was based on the lowest number of predictor variables, the smallest portion of the study area occurring within the likelihood surface area, and the most number of mapped burrows occurring within the likelihood surface.

_{10}distance to valley and elevation difference were the two variables used in fuzzy logic to predict the likelihood of each grid cell containing a burrowing owl burrow (Table 1). These two variables were selected from a pool of candidates, based on relatively larger magnitudes of differences between mean values where burrowing owls were and were not found, and based on their relatively lower level of shared variation as judged from examination of a correlation matrix and the output from principal components analysis.

**Table 1.**Fuzzy logic membership functions of grid cells belonging to the set of cells with burrowing owl burrows, based on a sample of 187,908 10 × 10 m

^{2}grid cells. For log

_{10}distance to valley, mean = 1.27302, SE = 0.11455, and SD = 0.46176, and for elevation difference, mean = 7.5846 and SE = 0.97564.

Value of variable Y for ith grid cell | Basis of membership function | Membership function of grid cell belonging to set with a burrowing owl burrow |
---|---|---|

Y = log_{10} distance to valley | ||

1.15847 < Y < 1.38757 | Within 1 SE of mean | 1 |

0.81126 ≤ Y ≤ 1.15847 | 1 SD to 1 SE < mean | 0.5 × (1 – cos(π × (Y − 0.81126) ÷ (1.15847 − 0.81126))) |

1.73478 ≥ Y ≥ 1.38757 | 1 SD to 1 SE > mean | 0.5 × (1 + cos(π × (Y − 1.38757) ÷ (1.73478 − 1.38757))) |

Y < 0.81126 or Y > 1.73478 | >1 SD away from mean | 0 |

Y = elevation difference | ||

5.63332 < Y < 9.53588 | Within 1 SE of mean | 1 |

3.68204 ≤ Y ≤ 5.63332 | Within 4 × SE; 2 × SE < mean | 0.5×(1 − cos(π × (Y − 3.68204) ÷ (5.63332 − 3.68204))) |

11.48716 ≥ Y ≥ 9.53588 | Within 4 × SE; 2 × SE > mean | 0.5×(1 + cos(π × (Y − 9.53588) ÷ (11.48716 − 9.53588))) |

Y < 3.68204 or Y > 11.48716 | >4 SE; away from mean | 0 |

_{10}distance to valley, the grid cell’s membership value in the burrowing owl burrow set was multiplied by 2.55 × 100, and based on elevation difference it was multiplied by 100 in order to obtain a value range that was easier to report and interpret. These two products were added and all sum values >70 were used to obtain the fuzzy logic surface because 70 appeared to be a natural break in the frequency distribution.

**Figure 6.**Example distribution of membership values in fuzzy logic set (red line), in this case for grid cells containing burrowing owl burrows as a function of log

_{10}distance to valley.

#### 3.4. Spatial Model Validation Using Burrowing Owl Locations in Vasco Caves

^{2}cells.

#### 3.5. Non-Spatial Models to Predict Burrowing Owl Fatalities

^{2}cell values mostly >5, (3) exhibited sensible gradients in measures of effect (i.e., relating observed to expected values) along a continuum such as elevation or rotor diameter, and (4) reasonably orthogonal.

^{2}tests, we scored wind turbines for their collision hazard to four raptor species that are the most often killed in the APWRA, because these species were the foci of mitigation measures required by Alameda County’s conditional use permits issued to the wind turbine companies (Table 2). Burrowing owls contributed to this scoring system, but the other three species obviously also affected the scores. The sum scores were aggregated into 4 groups per species, and then the aggregated scores were subjected to conditional statements (Table 3). The conditional statement considered natural breaks in ranges of sum scores specific to each species.

**Table 2.**Rating system to score APWRA wind turbines for collision hazard to four select species of raptor.

Rotor plane swept/s < 2,142 m^{2} | 1 |

Supporting tower is tubular or vertical axis | 1 |

Non-functional or next to derelict turbine or vacant tower | 1 |

Not part of a wind wall | 1 |

At the end of a turbine row | 1 |

In a canyon | 1 |

At or below 235 m elevation | 1 |

In valley (trending toward upwardly convex) | 1 |

Burrowing owl sum score | _____ (10 possible) |

Low reach of blades 8 to 9.6 mabove ground | 1 |

Fewer than 24 other turbines within 300 meters | 1 |

At the edge of a local cluster of turbines | 1 |

Not part of a wind wall | 1 |

At the end of a turbine row | 1 |

On a ridgeline | 1 |

In a canyon | 1 |

On steep slopes, >14° | 1 |

On slopes windward to one prevailing wind direction (NW or SW) and perpendicular to the other direction | 1 |

Golden eagle sum score | _____ (9 possible) |

At the end of a turbine row | 1 |

Fewer than 13 other turbines within 300 meters | 1 |

At the edge of a local cluster of turbines | 1 |

In a canyon | 1 |

On a ridgeline or ridge saddle | 1 |

On a northwest- or south/southeast-facing slope | 1 |

At or above 385 m elevation | 1 |

Red-tailed hawk sum score | _____ (6 possible) |

Rotor plane swept/s > 3,285 m^{2} | 1 |

On ridgeline or ridge saddle | 1 |

Below 135 m or above 385 m elevation | 1 |

American kestrel sum score | _____ (3 possible) |

**Table 3.**Conditional statements applied to the rating system for collision hazard to identify tiers of wind turbines grading from most hazardous (Tier 1) to least hazardous (Tier 5), where GOEA = golden eagle, RTHA = red-tailed hawk, BUOW = burrowing owl, and AMKE = American kestrel.

Index scores | Tier | No. of turbines | ||||||
---|---|---|---|---|---|---|---|---|

GOEA | Operator | RTHA | Operator | BUOW | Operator | AMKE | ||

4 | and | 4 | and | ≥1 | and | ≥1 | 1 | 22 |

≥1 | or | ≥1 | and | ≥3 | and | ≥3 | 1 | 124 |

≥3 | or | ≥3 | and | ≥4 | or | ≥4 | 1 | 75 |

4 | and | ≥3 | and | --- | and | --- | 2 | 45 |

--- | and | --- | and | ≥3 | and | ≥2 | 2 | 235 |

≥3 | and | ≥3 | and | ≥2 | and | ≥1 | 3 | 149 |

≥2 | and | ≥3 | and | ≥1 | and | --- | 3 | 323 |

≥3 | or | ≥3 | or | ≥3 | and | --- | 4 | 603 |

Else the turbine was assigned to Tier 5 | 5 | 2,498 |

## 4. Results

#### 4.1. Landscapes Used to Develop and Validate Predictive Spatial Models

**Table 4.**Principal Components following varimax rotation in PCA, showing only those rotated factor loadings >0.1.

Variable | Component 1 | Component 2 | Component 3 | |||
---|---|---|---|---|---|---|

APWRA | Vasco | APWRA | Vasco | APWRA | Vasco | |

ln Distance ratio | 0.984 | 0.979 | ||||

ln Elevation ratio | 0.907 | 0.921 | 0.133 | |||

log_{10} Distance to ridge | −0.872 | −0.851 | 0.312 | 0.332 | ||

log_{10} Distance to valley | 0.800 | 0.807 | 0.480 | 0.472 | ||

Gross slope | 0.908 | 0.909 | 0.175 | |||

Elevation difference | 0.831 | 0.775 | 0.440 | 0.549 | ||

Slope (percentage) | −0.119 | 0.829 | 0.745 | |||

Elevation | 0.214 | 0.437 | 0.627 | 0.234 | −0.191 | −0.211 |

log_{10} Total slope distance | 0.159 | 0.927 | 0.959 |

#### 4.2. Burrowing Owl Burrows Contributing to Model Development and Validation

**Figure 7.**Distribution of burrowing owl burrows used in Vasco Caves Regional Preserve for nesting in 2006 (green circles) and 2007 (orange circles), and for refuge in 2006 and 2007 (maroon circles).

#### 4.3. Burrowing Owl Relationships with Ground Squirrels and Slopes

**Table 5.**Mean comparisons among sets of grid cells with ground squirrel burrow systems, burrowing owl burrows, and neither ground squirrel nor burrowing owl burrows (empty cells) in the portions of the APWRA used to develop predictive models of burrowing owl burrow locations. Post-hoc least significant difference tests were denoted by a for tests between empty cell and ground squirrel, b for empty cell and burrowing owl, and c for ground squirrel and burrowing owl, and the overall ANOVA df = 1,187,907. Sample sizes were n = 185,077 for empty cells, n = 2,766 for cells with ground squirrels, and n = 65 for cells with burrowing owls.

Predictor variable | Mean | ANOVA F-value | Least-significant differences | ||
---|---|---|---|---|---|

Empty cell | Ground squirrel | Burrowing owl | |||

Distance to valley | 61.80 | 46.38 | 29.25 | 466.80** | abc |

Distance to ridge | 42.12 | 46.69 | 45.09 | 63.31** | a |

Total slope distance | 103.93 | 93.06 | 74.34 | 353.47** | abc |

Distance ratio | 5.40 | 3.33 | 1.45 | 92.27** | ab |

Elevation | 192.55 | 140.75 | 143.43 | 774.58** | ab |

Elevation difference | 17.12 | 12.98 | 7.71 | 176.64** | abc |

Elevation ratio | 5.43 | 2.86 | 1.20 | 274.78** | ab |

Gross slope (%) | 15.87 | 13.28 | 9.14 | 112.26** | abc |

Slope at grid cell (%) | 18.89 | 17.12 | 13.38 | 57.23** | abc |

Principal component 1 | 0.0049 | −0.3128 | −0.5301 | 146.83** | ab |

Principal component 2 | 0.0053 | −0.3433 | −0.6078 | 178.02** | abc |

Principal component 3 | 0.0027 | −0.1650 | −0.7678 | 57.53** | abc |

**Figure 8.**Mean and SE of percent of elevation from the bottom to the top of the slope on which the grid cell is located within Vasco Caves Regional Preserve. On average, ground squirrel burrow systems were lower on the slope than the average grid cell, and burrowing owl burrows, including nest burrows, were lower yet.

**Figure 9.**Compared to the average empty grid cell, those with ground squirrel burrows were relatively low on the slope (A), those with burrowing owl burrows were lower still (A), and those with ground squirrel burrows were on shallower slopes (B), and burrowing owl burrows were on even shallower slopes (B).

**Table 6.**Mean comparisons between sets of grid cells where burrowing owl burrows were not found and where they were found. Significance of ANOVA tests was denoted by *for P < 0.05 and ** for P < 0.005.

Predictor variable | Burrowing owl burrows | ANOVA F-value | |||
---|---|---|---|---|---|

Not found | Found | ||||

Mean | SD | Mean | SD | ||

Refuge & nest burrows APWRA-wide | |||||

Distance to valley (m) | 61.58 | 37.29 | 29.25 | 19.12 | 48.84** |

Distance to ridge (m) | 42.19 | 29.94 | 45.09 | 18.40 | 0.61 |

Total slope distance (m) | 103.77 | 30.16 | 74.34 | 22.01 | 61.86** |

ln Distance ratio | 0.46 | 1.63 | −0.53 | 1.18 | 23.69** |

Elevation (msl) | 191.79 | 97.35 | 143.43 | 38.18 | 16.04** |

Elevation difference (ridge − valley) | 17.06 | 12.18 | 7.71 | 8.02 | 38.30** |

Gross slope (%) | 16 | 10 | 9 | 7 | 30.85** |

Slope at grid cell (%) | 18.86 | 12.19 | 13.38 | 8.17 | 13.12** |

ln Elevation ratio | 0.60 | 1.67 | −0.27 | 0.87 | 17.52** |

PC 1, position on slope | 0.00 | 1.00 | −0.53 | 0.62 | 18.27** |

PC 2, slope steepness | 0.00 | 1.00 | −0.61 | 0.63 | 24.02** |

PC3, slope size | 0.00 | 1.00 | −0.77 | 0.93 | 38.34** |

Refuge & nest burrows in Vasco Caves | |||||

Distance to valley (m) | 59.07 | 40.60 | 34.73 | 23.97 | 47.02** |

Distance to ridge (m) | 59.53 | 41.89 | 85.80 | 28.90 | 51.40** |

Total slope distance (m) | 118.60 | 40.99 | 120.53 | 33.89 | 0.29 |

ln Distance ratio | 0.01 | 1.82 | −1.12 | 1.00 | 50.71** |

Elevation (msl) | 199.10 | 45.87 | 147.02 | 26.43 | 168.75** |

Elevation difference (ridge − valley) | 25.74 | 14.88 | 21.47 | 11.69 | 10.76* |

Gross slope (%) | 22 | 10 | 18 | 8 | 17.26** |

Slope at grid cell (%) | 27.07 | 12.55 | 23.87 | 9.75 | 8.50* |

ln Elevation ratio | 0.10 | 1.95 | −1.17 | 1.25 | 55.85** |

PC 1, position on slope | 0.00 | 1.00 | −0.74 | 0.55 | 72.33** |

PC 2, slope steepness | 0.00 | 1.00 | −0.47 | 0.74 | 29.26** |

PC 3, slope size | 0.00 | 1.00 | 0.27 | 0.74 | 9.48* |

Nest burrows in Vasco Caves | |||||

Distance to valley (m) | 59.00 | 40.58 | 43.27 | 33.31 | 6.16* |

Distance to ridge (m) | 59.60 | 41.88 | 83.37 | 32.73 | 13.19** |

Total slope distance (m) | 118.60 | 40.97 | 126.64 | 40.76 | 1.58 |

ln Distance ratio | 0.01 | 1.82 | −1.01 | 1.57 | 12.83** |

Elevation (msl) | 198.98 | 45.90 | 143.56 | 21.39 | 59.77** |

Elevation difference (ridge – valley) | 25.73 | 14.87 | 24.83 | 12.67 | 0.15 |

Gross slope (%) | 22 | 10 | 20 | 7 | 1.74 |

Slope at grid cell (%) | 27.06 | 12.54 | 24.61 | 10.63 | 1.56 |

ln Elevation ratio | 0.10 | 1.95 | −1.03 | 1.36 | 13.74** |

PC 1, position on slope | 0.00 | 1.00 | −0.70 | 0.75 | 19.98** |

PC 2, slope steepness | 0.00 | 1.00 | −0.34 | 0.69 | 4.66* |

PC 3, slope size | 0.01 | 1.00 | −0.21 | 0.96 | 6.55* |

#### 4.4. Spatial Model to Predict Burrowing Owl Locations

**Table 7.**The most efficient discriminant function models of grid cells predicted to include burrowing owl burrows, as well as a DFA model estimated from the PCA scores. All three models were significant (P < 0.0001).

Discriminant Functions (standardized canonical discriminant function coefficients) | Percent correct classification of grid cells | |
---|---|---|

Where burrowing owl burrows were found | Total | |

Total slope distance (0.77), Elevation difference (0.35) | 84.6 | 67.4 |

Elevation difference (0.82), ln Elevation ratio (0.50) | 87.7 | 62.3 |

Position on slope (0.48), Slope steepness (0.55), Slope size (0.69) | 72.3 | 72.8 |

**Figure 10.**Areas within part of the APWRA predicted to be selected by burrowing owls for burrow locations based on a Discriminant Function Model (A, magenta) and based on a Fuzzy Logic Model and surface values >70 (B), where the darker orange depict strongest prediction (bottom). Boundaries of some burrow mapping areas are shown by dark lines.

_{10}distance to valley using fuzzy logic. These were selected from different PCs and shared little variation (r = 0.27), so they were reasonably orthogonal. Of the 65 burrowing owl burrows in the APWRA study area, 57 (88%) were located on the FL surface based on FL values >10, composing 52% of the study area. Burrowing owl burrows were associated with the fuzzy logic surface (χ

^{2}= 33.17, df = 1, P < 0.001), and were mapped in it 1.69 times other than expected, i.e., Observed ÷ Expected = 1.69.

^{2}= 38.18, df = 1, P < 0.001), and were mapped in the FL surface 1.89 times other than expected.

#### 4.5. Spatial Model Validation Using Burrowing Owl Locations in Vasco Caves

**Figure 11.**Most of the mapped burrowing owl burrows at Vasco Caves overlapped the fuzzy logic surface depicted here in shades of purple.

#### 4.6. Relation of Predicted Burrowing Owl Locations to Wind Turbine Fatalities

**Table 8.**Fatalities off and on the sampled portions of the DFA and FL (values >70) surfaces across the APWRA, where the rated wind power capacity of turbines was 364.6 MW (84%) off the DFA surface and 71.5 MW (16%) on the DFA surface, and 340.0 MW (78%) off the FL surface and 97.8 MW (22%) on the FL surface. Significance of chi-square values were denoted by t for 0.10 > P > 0.05, * for P < 0.05, and ** for P < 0.005.

Species | Likelihood surface | |||||||
---|---|---|---|---|---|---|---|---|

Discriminant function analysis | Fuzzy logic | |||||||

Observed fatalities | Obs ÷ Expfatalities | Chi-square | Observed fatalities | Obs ÷ Expfatalities | Chi-square | |||

Golden eagle | Off | 46 | 1.04 | 36 | 0.92 | |||

On | 7 | 0.81 | 0.39 | 18 | 1.27 | 1.09 | ||

Red-tailed hawk | Off | 166 | 0.94 | 146 | 0.95 | |||

On | 46 | 1.32 | 4.38* | 67 | 1.18 | 2.03 | ||

American kestrel | Off | 47 | 0.95 | 37 | 0.87 | |||

On | 12 | 1.24 | 0.68 | 22 | 1.44 | 3.31 ^{t} | ||

Burrowing owl | Off | 49 | 0.85 | 34 | 0.73 | |||

On | 20 | 1.77 | 8.00** | 35 | 1.95 | 17.78** | ||

Barn owl | Off | 39 | 0.95 | 33 | 0.92 | |||

On | 10 | 1.25 | 0.58 | 16 | 1.28 | 1.10 | ||

Great horned owl | Off | 12 | 0.84 | 11 | 0.83 | |||

On | 5 | 1.80 | 2.11 | 7 | 1.58 | 1.65 | ||

Mallard | Off | 20 | 0.72 | 21 | 0.86 | |||

On | 13 | 2.41 | 12.76** | 12 | 1.49 | 2.30 | ||

Horned lark | Off | 21 | 1.09 | 15 | 0.90 | |||

On | 2 | 0.53 | 0.99 | 8 | 1.36 | 0.87 | ||

Western meadowlark | Off | 70 | 0.88 | 57 | 0.84 | |||

On | 25 | 1.61 | 6.85* | 39 | 1.56 | 8.42** | ||

Mourning dove | Off | 16 | 0.58 | 16 | 0.66 | |||

On | 17 | 3.15 | 29.74** | 18 | 2.17 | 13.01** | ||

Raptors | Off | 403 | 0.94 | 331 | 0.89 | |||

On | 111 | 1.32 | 10.20** | 186 | 1.38 | 20.92** | ||

Birds | Off | 828 | 0.86 | 695 | 0.84 | |||

On | 321 | 1.71 | 112.03** | 461 | 1.55 | 101.66** |

**Table 9.**Comparisons of adjusted fatality rate estimates within and outside fuzzy logic likelihood surfaces.

Species | Fatality rates (Fatalities/MW/yr) | |||||||
---|---|---|---|---|---|---|---|---|

1998–2003 | 2005–2007 | |||||||

Off FL surface | On FL surface | Off FL surface | On FL surface | |||||

Mean | SE | Mean | SE | Mean | SE | Mean | SE | |

Burrowing owl | 0.805 | 0.354 | 1.630 | 0.697 | 1.731 | 0.300 | 2.363 | 0.408 |

Golden eagle | 0.088 | 0.046 | 0.121 | 0.056 | 0.210 | 0.044 | 0.228 | 0.111 |

Red-tailed hawk | 0.299 | 0.066 | 0.270 | 0.070 | 0.703 | 0.096 | 0.810 | 0.129 |

American kestrel | 0.839 | 0.251 | 2.414 | 1.355 | 0.724 | 0.187 | 0.751 | 0.184 |

Barn owl | 0.050 | 0.015 | 0.064 | 0.029 | 0.194 | 0.037 | 0.300 | 0.093 |

Great horned owl | 0.013 | 0.006 | 0.010 | 0.005 | 0.053 | 0.028 | 0.094 | 0.034 |

Mallard | 0.075 | 0.030 | 0.081 | 0.041 | 0.120 | 0.070 | 0.049 | 0.037 |

Mourning dove | 0.000 | 0.000 | 0.000 | 0.000 | 0.089 | 0.042 | 0.126 | 0.086 |

Western meadowlark | 3.261 | 1.379 | 2.073 | 0.668 | 2.960 | 0.435 | 3.567 | 0.691 |

Raptors | 2.102 | 0.745 | 4.517 | 2.217 | 3.818 | 0.812 | 4.680 | 1.048 |

Birds | 12.510 | 6.004 | 12.405 | 5.706 | 15.001 | 3.839 | 19.970 | 5.643 |

#### 4.7. Non-Spatial Models to Predict Burrowing Owl Fatalities

## 5. Discussion

**Figure 12.**Mean fatality rates by collision hazard Tier classification for burrowing owl, American kestrel, golden eagle, and red-tailed hawk estimated from fatality monitoring in 1998–2003 and 2005–2007 in the Altamont Pass Wind Resource Area. Hatched bars in the top left graph represent percentages of the APWRA’s installed capacity in each Tier, e.g., Tier 1 turbines represented 4.4% and 4.7% of the APWRA’s installed capacity in 1998-2003 and 2005–2007, respectively.

**Table 10.**Calculated shifts in fatality rates if all wind turbines on DFA or FL surfaces were moved off the surface.

Species | Percent change in annual APWRA fatalities if all turbines were moved to: | |||
---|---|---|---|---|

Off FL surface | Tier 4 and 5 locations | |||

1998–2003 | 2005–2007 | 1998–2003 | 2005–2007 | |

Burrowing owl | −22 | −10 | −27 | −5 |

Golden eagle | −9 | −3 | −37 | −27 |

Red-tailed hawk | +3 | −4 | −13 | −9 |

American kestrel | −34 | −1 | −51 | −13 |

Barn owl | −7 | −14 | −55 | −7 |

Great horned owl | +7 | −14 | −34 | −11 |

Raptors | −24 | −6 | −36 | −7 |

Birds | 0 | −9 | −16 | −8 |

**Table 11.**Calculated percent reductions in annual fatalities per MW of wind turbines relocated from more hazardous to less hazardous parts of the APWRA.

Species | Percent fatality reduction per MW of turbines moved to: | |||
---|---|---|---|---|

Off FL surface | Tier 4 and 5 locations | |||

1998–2003 | 2005–2007 | 1998–2003 | 2005–2007 | |

Burrowing owl | 0.14 | 0.06 | 0.18 | 0.04 |

Golden eagle | 0.06 | 0.01 | 0.25 | 0.21 |

Red-tailed hawk | −0.02 | 0.03 | 0.09 | 0.07 |

American kestrel | 0.21 | 0.01 | 0.35 | 0.10 |

Barn owl | 0.05 | 0.08 | 0.38 | 0.05 |

Great horned owl | −0.04 | 0.11 | −0.23 | −0.08 |

Raptors | 0.15 | 0.04 | 0.25 | 0.06 |

Birds | 0.00 | 0.05 | 0.11 | 0.06 |

## Acknowledgments

## References

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## Share and Cite

**MDPI and ACS Style**

Smallwood, K.S.; Neher, L.; Bell, D.A. Map-Based Repowering and Reorganization of a Wind Resource Area to Minimize Burrowing Owl and Other Bird Fatalities. *Energies* **2009**, *2*, 915-943.
https://doi.org/10.3390/en20400915

**AMA Style**

Smallwood KS, Neher L, Bell DA. Map-Based Repowering and Reorganization of a Wind Resource Area to Minimize Burrowing Owl and Other Bird Fatalities. *Energies*. 2009; 2(4):915-943.
https://doi.org/10.3390/en20400915

**Chicago/Turabian Style**

Smallwood, K. Shawn, Lee Neher, and Douglas A. Bell. 2009. "Map-Based Repowering and Reorganization of a Wind Resource Area to Minimize Burrowing Owl and Other Bird Fatalities" *Energies* 2, no. 4: 915-943.
https://doi.org/10.3390/en20400915