Background Mortality of Wildlife on Renewable Energy Projects

Simple Summary

Background mortality averaged 0 bats and 0.0055 birds/ha among study sites far from wind turbines, but background mortality estimated from searches centered on sites where turbines had been removed was 3.5 times higher at 0.0194 birds/ha. Based on a comparison of estimated fatalities/ha before and after turbines were removed, background mortality was 3.6%, ancillary infrastructure contributed 8.2%, and wind turbines contributed 88.2% to estimates of bird mortality in a Wind Resource Area.

Abstract

With the expansion of utility-scale renewable energy development worldwide, accurate estimation of bird and bat fatalities is needed for informed policy-making and appropriate formulation of mitigation strategies. Background mortality, or the mortality caused by natural as opposed to anthropogenic processes, is often identified as a positive bias, and sometimes it is identified as a substantial or even leading contributor to fatality estimates. To estimate background mortality, I compiled fatalities/ha counted during searches of turbine-free study sites reported by others over 2548 ha and myself over 2297 ha. No bat fatalities were found in any of these searches. Bird fatalities/ha averaged 0.0055. I also compared estimates of fatalities/ha before and after turbine removals from 123 rows of wind turbines in California’s Altamont Pass Wind Resource Area (APWRA). These turbine rows had been searched for fatalities over various periods during 1998–2002 and 2006–2014, and fatalities had been recorded at each row during first searches of new monitoring periods. I used the same search methods as the monitor, but my first searches covered 624 ha of plots centered around vacant turbine sites. I found 0.0194 (95% CI: 0.0035–0.0352) bird fatalities/ha, but no bat fatalities. I estimated that background mortality was 3.6% (95% CI: 0–6.2%), mortality caused by unremoved power lines and meteorological towers was 8.2% (95% CI: 0–15.8%), and mortality caused by wind turbines was 88.2% (95% CI: 78–100%). Contamination of carcasses from operable wind turbines ≥ 400 m distant from vacant turbine sites likely biased my estimate upward by 3.5-fold compared to natural mortality averaged among sites far from wind turbines. This study does not support the notion that background mortality contributes substantially to mortality estimates at renewable energy projects.

1. Introduction

Utility-scale renewable energy has increased worldwide to meet increasing demand, but annual collision mortality caused by USA wind energy alone was estimated in the hundreds of thousands of birds [1,2,3] and bats [1,4,5], and the estimated bird mortality is expected to increase to millions of birds with the increased installed capacity over the next decade. Utility-scale solar projects, along with their generation tie-ins, above-ground distribution lines, and security fences, have been estimated to result in similar per-MW rates of collision mortality of birds but lower rates of bats [6]. The level of mortality caused by renewable energy needs to be known with reasonable accuracy to weigh the costs and benefits of each form of energy generation and to inform project siting and mitigation strategies. Impinging on the accuracy of fatality estimates are levels of fatality search effort and a suite of negative biases related to fatalities not found by searchers [7]. Research to detect more fatalities of those available to searchers focused on search interval [8,9] and use of dogs instead of humans [10,11,12]. Research also targeted more realistic fatality detection trials [13], representation of unsearchable areas [14,15,16] and unsearchable conditions [17], and the spatial distribution of fatalities relative to the maximum search radius [1,18,19]. Advances from these research efforts would tend to increase fatality estimates. But erroneously double-counting fatalities or miscounting fatalities caused by a different source would introduce positive biases to the mortality estimate.

Often cited as a positive bias that results in overestimation of mortality caused by renewable energy projects is background mortality: the proportion of fatalities—as determined by carcasses or remains—in a fatality estimate that was caused by natural phenomena such as predation, exposure to inclement conditions, disease, or age [3,7,20,21]. Natural mortality was originally identified as the baseline against which to measure effects of wind turbines [22,23]. Soon after, investigators referred to this baseline natural mortality as background mortality [7,24,25,26]. Background mortality has also been attributed to pre-existing tall towers, large numbers of wires, farming activity, or busy roads [27,28], but herein I regard these mortality sources as potentially confounding anthropogenic factors and not as background mortality.

The magnitude of positive bias of background mortality has been characterized as so low as to not justify quantification [29], and, alternatively as substantial [21]—one of the most substantial biases associated with fatality estimates at wind projects [24] and at utility-scale solar projects [30]. The California Wind Energy Association asserted that background mortality exceeded that of wind turbines (24 October 2006 letter to J. Pfannenstiel, Chair, California Energy Commission). A consultant asserted that only 20% to 35% of fatalities found by Smallwood and Thelander (2008) died from collisions with wind turbines; the remainder presumably consisted of background fatalities [31]. A wind company in the Altamont Pass Wind Resource Area (APWRA) claimed that background mortality contributed up to 100% of fatalities of select raptor species originally attributed to wind turbines [32]. Similarly, authors of fatality monitoring reports have qualified their findings as overestimates due to background mortality, often citing a study in which background fatalities found on reference plots numbered a third of fatalities found at wind turbines [33]. It is noteworthy that Johnson (2007), based on using a combination of Breeding Bird Survey data, selected published natural mortality estimates, and unpublished data, concluded that background mortality may exceed wind energy mortality [34]. However, this approach to estimating background mortality has not been critically evaluated and may be flawed due to a number of different biases incorporated by using multiple unrelated data sets. Background mortality is only that portion of natural mortality which can be found by fatality searchers and accurately assigned as wind turbine collision victims.

Background mortality has also been used to downplay the need to adjust mortality estimates for the negative bias of insufficient search areas around wind turbines. In response to the search radius bias addressed in [1], it was suggested that the positive bias of background mortality “…partially or even completely offset any bias associated with plot size” [3]. Given that the negative bias of plot size was estimated to average about 10% among wind projects [1], background mortality, according to [3], would contribute to a ≥10% overestimate of wind turbine-caused fatalities. However, the earlier estimates of search radius bias varied by combination of plot size and tower height, ranging up to 38% among wind projects monitored through 2010 [1]. The perceived negative bias of insufficient search area is likely to shift with increases in plot size applied to a given wind turbine tower height [12]. Furthermore, compared to human searchers, scent-detection dogs found more fatalities located farther from wind turbines mounted on 80 m towers; models fit to patterns of fatalities found by dogs predicted 20% of birds and 14% of bats were deposited outside the 105 m maximum search radius around wind turbines mounted on 80 m towers [12]. Therefore, for the assertion to be correct that background mortality offsets any bias associated with plot size [3], background mortality would need to apply to ≥14% of bat fatalities and 20% of bird fatalities. There remains an obvious need to quantify background mortality to determine whether it qualifies as a substantial positive bias.

To isolate the effects of background mortality, investigators must distinguish fatalities caused by natural phenomena from those caused by renewable energy infrastructure, where infrastructure can consist of wind turbines and meteorological towers, PV solar panels or heliostat mirrors, and overhead power lines, security fences, power towers, and other buildings erected in support of solar thermal projects. This distinction could be made by accurately assigning cause of death to fatality remains [35], or, alternatively, by inferring the effects of renewable energy infrastructure based on fatality searches performed as part of a before–after or an impact-control (BACI) experiment [36,37].

The accurate assignment of cause of death to fatality remains becomes increasingly more difficult the longer the time interval between periodic fatality searches, because wind turbines sometimes dismember carcasses and because initially intact carcasses are scavenged, and either of these outcomes gives fatality searchers the impression that predation caused the death. Some have reasoned that because most fatality searchers identify feather piles as fatalities, and because feather piles result from feeding by predators, then it follows that most fatality estimates are biased high due to the misassignment of predation victims as fatalities caused by renewable energy infrastructure [38,39]. However, scavengers often leave carcass remains in the same condition as left by predators, and scavengers are often the same species as predators. Accurately accounting for background mortality in estimates of mortality at renewable energy facilities is difficult [40], and it will remain elusive so long as investigators rely on interpretation of fatality remains to assign cause of death.

Compared to the approach of assigning cause of death to fatalities, experimental inference should more effectively isolate the effects of background mortality in mortality estimates. Two experimental designs have been proposed and implemented to estimate background mortality: preconstruction searches for fatalities on the same plots to be searched post-construction as part of impact measurement [6], and fatality searches on equal-sized reference or control plots located near enough to impact plots overlapping renewable energy infrastructure to be relevant but sufficiently distant from the infrastructure to minimize contamination from animals killed by the infrastructure and miscounted as background mortality [41]. Both approaches could introduce bias, however. Preconstruction designs could introduce bias caused by changes in fatality detection rates following construction grading and removal of some of the vegetation cover for turbine pads, solar arrays, and access roads. Reference site designs could introduce bias where reference sites are located too close to renewable energy infrastructure to avoid contamination by collision victims or where wind, ground cover, or terrain differ sufficiently from the renewable energy infrastructure that volant animal species and their abundances yield incomparable background mortality. A third approach presented itself to me in the APWRA when thousands of first-generation wind turbines were removed for repowering to modern wind turbines. Among the vacated turbine sites (“addresses”) that I judged sufficiently distant from repowered wind turbines, turbine pads and their access roads remained unchanged from earlier years when biologists searched for fatalities, so the presence of animal remains should have been just as detectable and unchanged. These vacated addresses presented the additional benefit of a record of fatality finds over 13 years of fatality searches at operational turbines [39] that could be directly compared to fatalities found in subsequent background mortality searches.

My study goal was to establish whether background mortality warrants further consideration in the assessment of renewable energy impacts on volant wildlife, and if so, then to estimate an appropriate adjustment to mortality to offset its positive bias. My objectives were to estimate mean background mortality from reports of fatality searches at wind energy projects, estimate background mortality from my own collection of large-area searches where renewable energy infrastructure was absent, and compare estimates of fatalities/ha before and after wind turbines were removed in the APWRA. Because electric distribution lines and meteorological towers remained among some of the rows of turbine-free search plots when I performed background mortality searches, another objective was to estimate the effect of remaining infrastructure on fatality rates measured in background mortality searches.

2. Materials and Methods

2.1. Study Areas

There were two types of study areas where I searched for background wildlife fatalities: (1) 20 sites throughout California and western Nevada that never had renewable energy facilities and were nowhere near such facilities, and (2) sites within the APWRA where wind turbines had operated but had been recently decommissioned and removed. At the 20 sites without renewable energy facilities, soils and vegetation cover varied greatly and included annual and perennial grasses, scrub and shrub communities, riparian and woodland, and formerly disturbed areas lacking overhead hazards to volant wildlife such as power lines.

At the second type of site, I compared fatalities/ha found at turbine addresses before and after turbine removals in the APWRA, in Alameda and Contra Costa Counties, California (Figure 1 and Figure 2). The wind turbines that were removed had operated since their installations in the early 1980s, and they ranged from 40 KW to 330 KW in rated capacity (

Table 1. Wind turbine removals from the Altamont Pass Wind Resource Area, Alameda, and Contra Costa Counties, California, and dates of first background mortality surveys, 2017–2019.

Projects or Regions within the APWRA	Turbine Removals		Background Mortality Searches
	Size and Model	Dates Completed	First Date	Fewest Months Since Removals
Northwind	65 kW Nordtank	August 2014	4 July 2018	47
Tres Vaqueros	330 kW Howden	March 2017	15 February 2018	11
Buena Vista east side	200 kW Windmaster	June 2007	20 April 2018	10
Summit Winds, north	100 kW KCS-56	June 2016–September 2017	29 November 2017	17
Summit Winds, west	100 kW KCS-56	September 2016–April 2017	27 November 2017	14
Golden Hills North	120 kW Bonus, 100 kW KCS-56	August 2017	31 May 2018	9
North Sand Hill	100 kW KCS-56	September 2016	11 April 2018	19
Altech	40 kW Enertech	September 2017	25 April 2018	7
Taxvest	65 kW Micon	September 2017	25 April 2018	7
Venture	109 kW Polenko	September 2017	4 June 2018	9
Mountain House	65 kW Micon	September 2018	28 March 2019	6
Santa Clara	95 kW Vestas	9 October 2017	4 April 2018	6
Midway	100 kW KCS-56	March 2017	8 February 2018	11
Gate 9	100 kW KCS-56	December 2013–March 2017	2 May 2018	14

). These turbines were mounted on both tubular and lattice towers that ranged from 14 m to 43 m in height. The turbines were arranged in rows, often sited along ridge crests. The APWRA was situated on steeply rolling hills covered by cattle-grazed annual grasses. California ground squirrels (Otospermophilus beecheyi) occurred in colonies covering much of the lower elevation portions of the APWRA and focused activity of predatory species such as golden eagle (Aquila chrysaetos), coyote (Canis latrans), American badger (Taxidea taxus), Pacific gopher snake (Pituophis c. catenifer), and northern Pacific rattlesnake (Crotalus o. oreganus). Potential scavengers of carcasses in the APWRA also include common ravens (Corvus corax), turkey vultures (Cathartes aura), red-tailed hawks (Buteo jamaicensis), great horned owls (Bubo virginianus), red foxes (Vulpes vulpes), gray foxes (Urocyon cinereoargenteus), and striped skunks (Mephitis mephitis). Bats and birds migrated through the APWRA with a distinct peak in fall [42]. Elevations ranged from 41 to 477 m.

2.2. Background Mortality Estimates

I used 3 data sets to estimate background mortality, the second and third of which were derived from the study areas summarized above and described in the next paragraphs. The first data set was a compilation of fatalities/ha found on wind energy preconstruction sites or on turbine-free reference sites among fatality monitoring reports I reviewed. Two of the reports were of preconstruction searches for animal carcasses at planned wind turbine sites [43,44], and six were of searches at reference sites associated with monitored wind projects [45,46,47,48,49,50].

The second data set was a compilation of fatalities/ha I found during my own searches for background fatalities at 20 study sites throughout California and Nevada, 1999–2019 (Figure 3). These sites were covered in forests, perennial and annual grasslands, vernal pool complexes, reclaimed gravel mines, deserts, and riverine environments. At these sites I walked parallel transects spaced 12–15 m apart. From these 20 study sites, I calculated fatalities/ha.

The third data set was based on fatality searches before and after turbine removals in the APWRA (Figure 3), but the data set was built only from first searches, as described below. Biologists searched rows of wind turbines during 1998–2002 [51] and 2005–2014 [39]. Each row of searched turbines included a first search followed by a series of periodic searches. Periodic searches continued uninterrupted for years at some turbine rows, but they stopped and resumed at others. First searches included the very first searches of particular wind turbines and searches following gaps of >85 days since the last fatality search at the turbines (n = 16,137). Thus, some turbine rows underwent one first search, and others underwent 2–5 first searches over the years. To maximize comparability of the findings of these past fatality searches to my background fatality searches, I used fatality finds only from the first searches at the same turbine addresses that I searched for background fatalities. My background fatality searches began at vacant turbine addresses ≥ 6 months after the turbines were removed.

I selected all accessible rows of vacant wind turbine addresses that included histories of first-search fatality finds during the period 1998–2014 and which were located ≥400 m from operable wind turbines. Data collected ≥6 months after turbine removals were from my searches of vacant turbine addresses (Table 1). I searched rows of turbine addresses where fatalities had been found by the monitor when the turbines operated. I used the same methods and standards used by the fatality monitor, including fatalities estimated <90 days since death and composed of ≥2 flight feathers connected by tissue or ≥5 scattered flight feathers, ≥10 contour feathers, bones, body parts, or whole carcasses. I walked parallel transects 4–6 m apart to a maximum distance of 50 m from all turbines except Howden turbines, from which I searched to a distance of 60 m. I searched 1285 vacant turbine addresses arranged in 124 rows. Among 47 vacant turbine rows that remained available to me through the study, I repeated searches at 42 of them and repeated searches twice at another 5 with an average time elapsed since the previous search of 276 days (95% CI: 245–307, range 78–566 days). Of the turbine addresses where fatality searches were directly comparable before and after turbines were removed, biologists had previously performed 5099 first searches, and I subsequently performed 1939 first searches.

2.3. Bias Identification and Adjustment

To reveal whether fatalities found in first searches before and after turbine removals reflected any potential detection bias from variation in body size, I compared proportions of fatalities in 0.5 m increments of body mass between first searches and various methods of routine fatality monitoring. Specifically, I compared proportions of fatalities across body mass increments yielded by the following methods: by first searches before and after turbine removals to those yielded by scent-detection dogs at neighboring wind projects [12]; by humans who searched first-generation turbines at 2-day intervals over 2 months in fall 2007 and 2 months in spring 2018 (Alameda County, unpublished data); by 5-day intervals over 3 years [13]; by 7–15-day and 28–30-day intervals over various time periods across several APWRA projects [52,53,54]; and by 31–60-day intervals throughout the APWRA from 1998 to 2014 [39,51]. Additionally, leashed scent-detection dogs and their handlers searched for fatalities at modern wind turbines in the Buena Vista and Golden Hills Wind Energy Projects, both projects located within the APWRA. Dogs and their handlers achieved carcass detection rates of 96% for bats and 90% for small birds [12], so their body-size distribution of detected fatalities served as a useful benchmark against which to assess results from human searchers. After dogs, detection rates of bats and small birds can be expected to decline from humans searching at 2-day, 5-day, and increasingly longer intervals. To adjust for variation in detection rates by body size, I developed empirical models from carcass detection trials to predict detection rates from body size (see below).

I estimated the mean number of fatalities/ha, $\widehat{F}$, in all data sets by adjusting the number of fatalities/ha found, F, by the proportion of fatalities not found, D: $\widehat{F}={\displaystyle \frac{F}{D}}$, where D was predicted for a 39-day search interval based on detection trial outcomes that I logit-regressed on body mass (g) [9,13]. I represented D for a 39-day search interval because it was based on many detection trial outcomes [9], and it would have changed little through 90 days—the time-since-death threshold I used to determine whether to include a fatality in $\widehat{F}$. I used the delta method to combine standard errors of F and D and to calculate 95% confidence intervals [12].

I compared $\widehat{F}$ before and after turbine removals between two groups of vacant turbine addresses, one with and one without infrastructure such as electric distribution or transmission lines and meteorological towers that remained after the turbine removals. I measured percentage background mortality as ${\left({\displaystyle \frac{{\widehat{F}}_A}{{\widehat{F}}_B}}\times 100\%\right)}_0$ and percentage mortality caused by infrastructure remaining after turbine removals as ${\left({\displaystyle \frac{{\widehat{F}}_A}{{\widehat{F}}_B}}\times 100\%\right)}_0-{\left({\displaystyle \frac{{\widehat{F}}_A}{{\widehat{F}}_B}}\times 100\%\right)}_{+}$, where subscripts B and A represented before and after turbine removals, respectively, and subscripts + and 0 represented groups of turbine addresses with and without infrastructure after turbine removals.

3. Results

3.1. Fatality Searches on Turbine-Free Areas

Eight reports of background mortality averaged 0.0117 (95% CI: 0.0034–0.0201) bird fatalities/ha across 2674 ha (

Table 2. Bird carcasses found per ha in background mortality surveys reported in fatality monitoring reports.

Study	Study Site	Search Plots			Sum ha	Carcasses
		n	ha	Visits		n	n/ha
[43] a	Norris Hill, Montana				579.60	4	0.0069
[44]	High Winds, Solano County, California	90	1.77	1	159.04	0	0.0000
[33] b	Buffalo Ridge, Minnesota	30	1.59	23	1097.00	10	0.0091
[45]	Ponnequin, Weld County, Colorado		24.00	5	120.00	2	0.0167
[46] c	Buffalo Mountain, Tennessee	3	0.79	150	353.40	2	0.0057
[47]	National Wind Technology Center, Colorado	10	0.79	24	189.60	0	0.0000
[48] d	San Gorgonio Phase I, California	40	0.79	4	125.66	7	0.0557
[49,50]	Forward Energy Center, Wisconsin	3	0.49	69	33.52	0	0.0000
Total	All studies included				2674.20	25	0.0117
Total	Study 7 excluded				2548.50	18	0.0055

^a Preconstruction surveys along 289.8 km of transects; I assumed 10 m searched for either side. ^b Because the reporting was difficult to follow, I used data only from the ‘Reference Area’. ^c One control plot 160 m from turbines, 2 others near the met tower. ^d Background mortality plots located 400–2000 m from turbines, but bird carcasses found on these plots appeared to have been killed by wind project infrastructure such as power lines and traffic on roads.

), or twice the mean of 0.0055 (95% CI: 0.0010–0.0100) bird fatalities/ha I found across 2297 ha (

Table 3. Background mortality search efforts among study sites without renewable energy facilities across California and the Amargosa Valley in Nevada, 1999–2019.

Study Site	Vegetation Cover	Year (s)	Search Effort			Carcasses
			Ha	Visits	Sum ha	n	n/ha
Amargosa Valley, Nevada	Creosote	2009	54.31	1	54.31	0	0
Bear Creek, Colusa County	Annual grassland bordering riverine	1999	6.27	1	6.27	0	0
Cholame River near Shandon, San Louis Obispo County	Foothill riparian	1999	6.79	1	6.79	0	0
Carmel River at Pacific Ocean	Foothill riparian	2000	15.12	1	15.12	0	0
Decommissioned landfill, Concord Naval Weapons Station	Ruderal scrub	2005	5	1	5.00	0	0
Hills of Concord Naval Weapons Station	Annual grassland	2006	15.06	1	15.06	0	0
Wildlife Management Zone, Dixon National Radio Transmission Facility, Solano County	Annual grassland and vernal pool	2009	62.1	1	62.10	1	0.0161
Antenna field, Dixon National Radio Transmission Facility, Solano County	Annual grassland and vernal pools	2006–2011	85.2	6	511.20	6	0.0117
Goat Mountain, Lake County	Meadows	1999	37.32	1	37.32	0	0
Highway 41 and Jackson Ave., Kings County 1	Iodine bush, saltgrass	2002–2013	21–38.1	12	269.10	1	0.0037
Resource Management Area 4, Naval Air Station, Lemoore	Annual grassland	2003	42.16	1	42.16	0	0
Resource Management Area 5, Naval Air Station, Lemoore 2	Annual grassland	1999–2013	43.1	22	948.20	20	0.0211
Marsh Creek Preserve, Contra Costa County	Oak woodland, annual grassland	2019	110.1	1	110.10	4	0.0363
Vaquero Farm sampling plots (n = 7), Contra Costa County	Annual grassland	2018	47.506	1	47.51	1	0.0210
Private ranch northeast of Prunedale	Coastal oak woodland	2001	3.78	1	3.78	0	0
San Joaquin River north of Merced	Riparian, grassland	2001	1.9	1	1.90	0	0
Ranches north of Fresno	Annual grassland	2000	36.46	1	36.46	0	0
Fresno gravel pits (n =4)	Ruderal	2000	41.3	1	41.30	0	0
Petaluma ranch	Annual grassland	2000	1.49	1	1.49	0	0
Mather Air Force Base	Annual grassland and vernal pools	1999–2002	16.28	5	81.40	0	0
Total all sites					2296.55	33	0.0055

¹ Hunters occasionally shot birds here. ² Shotgun shells found near western kingbird and northern mockingbird carcasses in 2012.

). However, excluding Anderson et al.’s (2005) background mortality estimate [48], where reference sites were potentially contaminated by fatalities (mostly old remains) originating from the wind turbines and associated infrastructure, the mean fatalities/ha declines 53% to 0.0055 (95% CI: 0.0009–0.0101). No bat carcasses were found in the 4971 ha of searches (Table 2 and Table 3).

At background fatality survey plots located far from any renewable energy projects, I found many fewer carcasses than typically found at renewable energy projects. At Dixon National Radio Transmission Facility, Solano County, California, I found a mourning dove (Zenaida macroura) carcass during one search of the 62.1-ha Wildlife Management Zone, which lacked any overhead hazards. On the antenna field portion of the Dixon National Radio Transmission Facility, where aerial cables of the antenna arrays posed collision hazards, 6 years of searches yielded carcasses of 4 burrowing owls, 1 black-necked stilt (Himantopus mexicanus), and 1 red-winged blackbird (Agelaius phoeniceus).

Southeast of the intersection of Highway 41 and Jackson Avenue, Kings County, California, I found 1 red-tailed hawk carcass in 1 search/year over 12 years. My first search at this site covered 38.1 ha, but a property owner installed a fence and reduced access to 22 ha thereafter. In one search of the 110-ha Marsh Creek Preserve, Contra Costa County, California, I found 4 carcasses, including 1 red-tailed hawk (Buteo jamaicensis), 1 American barn owl (Tyto furcata), 1 mourning dove (Zenaida macroura), and 1 dark-eyed junco (Junco hyemalis). On 5 plots I searched on Vaquero Farms in 2018, I found 1 mourning dove carcass.

Hunting and possible poaching complicated my estimation of background mortality at one site. After 22 searches of the 43.1-ha Resource Management Area 5 on Naval Air Station, Lemoore, I found fatality remains of 4 red-tailed hawks, 1 Swainson’s hawk (Buteo swainsoni), 1 Cooper’s hawk (Accipiter cooperii), 1 northern harrier (Circus cyaneus), 4 mourning doves, 4 American barn owls, 1 great horned owl, 1 great egret (Ardea alba), 1 western kingbird (Tyrannus verticalis), 1 loggerhead shrike fledgling, 2 northern mockingbirds (Mimus polyglottos), 2 song sparrows (Melospiza melodia), and 4 unidentified small birds. I reduced the number of fatalities included in background mortality estimation to 20 after omitting the loggerhead shrike fledgling, which would not have been included in fatality estimation had it been found on a renewable energy project, and after omitting 3 mourning doves, 1 western kingbird, 1 northern mockingbird, 1 American barn owl, and 1 great horned owl whose carcasses I found amid many shotgun shells during the mourning dove hunting season. All but 11 of the remaining bird fatalities might also have been caused by both legal and illegal shooting during the mourning dove hunting season, but I lacked evidence to justify omitting them.

3.2. Before and After Turbine Removals

Where infrastructure remained following turbine removals, I searched 146.9 ha centered around 414 vacant addresses that formerly supported 36.15 MW of operable turbines arranged in 41 rows. With repeat searches at some addresses, I cumulatively performed 520 vacant address searches covering 197.02 ha, where the biologists who earlier searched for fatalities from 1998 to 2014 cumulatively performed 1518 first turbine searches covering 578.47 ha. No bat carcasses were found by first searches at these turbines before or after turbine removals. Whereas the first searches of 1998−2014 detected 0.2736 (95% CI: 0.2167–0.3304) bird fatalities/ha while operable turbines were present, I detected 0.0464 (95% CI: 0.0146–0.0782) bird fatalities/ha long after turbines were removed (

Table 4. First-search fatalities/ha before and after turbine removals, Altamont Pass Wind Resource Area, California, 1998–2019.

Species	Fatalities/ha
	Where Infrastructure Remained				Where No Infrastructure Remained
	Before Turbines Removed		After Turbines Removed		Before Turbines Removed		After Turbines Removed
	n	$\overline{x}$	n	$\overline{x}$	n	$\overline{x}$	n	$\overline{x}$
Mexican free-tailed bat (Tadarida brasiliensis)	6	0.0091			1	0.0008
Mallard (Anas platyrhynchos)					6	0.0065
Ring-necked duck (Aythya collaris)					1	0.0003
Cattle egret (Ardea ibis)					1	0.0004
Turkey vulture (Cathartes aura)					2	0.0013
Golden eagle (Aquila chrysaetos)	8	0.0124	1	0.0031	21	0.0185
Red-tailed hawk (Buteo jamaicensis)	21	0.0463			58	0.0508	1	0.0065
Ferruginous hawk (Buteo regalis)					4	0.0033
Buteo	3	0.0055			7	0.0095
American kestrel (Falco sparverius)	5	0.0107			9	0.0102
Prairie falcon (Falco mexicanus)					2	0.0029
Raptor	2	0.0037			1	0.0007
American avocet (Recurvirostra Americana)	2	0.0037
Ring-billed gull (Larus delawarensis)					2	0.0030
California gull (Larus californicus)			2	0.0037	1	0.0004	2	0.0031
Gull (Laridae)	3	0.0097			16	0.0110
Rock pigeon (Tyto alba)	33	0.0495	2	0.0069	53	0.0664
Mourning dove (Zenaida macroura)	2	0.0019			3	0.0042
American barn owl (Tyto furcata)	4	0.0100	2	0.0145	16	0.0157	1	0.0008
Great horned owl (Bubo virginianus)	2	0.0028			3	0.0019
Burrowing owl (Athene cunicularia)	10	0.0186	1	0.0103	2	0.0019	1	0.0022
White-throated swift (Aeronautes saxatalis)					1	0.0009
Northern flicker (Colaptes auratus)					2	0.0005
Loggerhead shrike (Lanius ludovicianus)					2	0.0043
American crow (Corvus brachyrhynchos)					1	0.0005
Common raven (Corvus corax)	1	0.0009			7	0.0133	1	0.0009
Horned lark (Eremophila alpestris)	1	0.0021	1	0.0025	2	0.0033	1	0.0010
Cliff swallow (Hirundo pyrrhonota)	2	0.0022
Mountain bluebird (Sialia currucoides)	1	0.0034			1	0.0006
European starling (Sturnus vulgaris)	8	0.0137			25	0.0239
American pipit (Anthus rubescens)							1	0.0008
Savannah sparrow (Passerculus sandwichensis)					2	0.0011
Red-winged blackbird (Agelaius phoeniceus)	3	0.0069
Western meadowlark (Sturnella neglecta)	11	0.0216			19	0.0198	1	0.0011
Brewer’s blackbird (Euphagus cyanocephalus)	2	0.0024			2	0.0024
Blackbird (Icteridae)							1	0.0029
House finch (Carpodacus mexicanus)	4	0.0064			3	0.0034
Small bird	10	0.0201	2	0.0054	10	0.0119
Medium bird					6	0.0121
Large bird	3	0.0100			7	0.0062
All birds	147	0.2736	11	0.0464	299	0.3130	10	0.0194

). Where infrastructure remained, I found 17% (95% CI: 6.7–23.7%) of the number of fatalities/ha as had the monitor when turbines were operable. I found 2.4 times the number of bird fatalities/ha among vacant turbine addresses with infrastructure as compared to those without infrastructure.

No infrastructure remained where I searched 260.9 ha centered around 847 vacant addresses that formerly supported 85.852 MW of operable turbines arranged in 82 rows. With repeat searches at some addresses, I cumulatively performed 1419 vacant address searches covering 426.84 ha, where the biologists who earlier searched for fatalities from 1998 to 2014 cumulatively performed 3581 first turbine searches covering 1091.57 ha. The first searches of 1998−2014 found 1 Mexican free-tailed bat fatality before turbines were removed, but I found none after turbines were removed. Whereas the first searches of 1998−2014 detected 0.3130 (95% CI: 0.2457–0.3802) bird fatalities/ha in the presence of operable turbines, I detected 0.0194 (95% CI: 0.0035–0.0352) bird fatalities/ha long after turbines were removed (Table 4). Where no infrastructure remained, I found 6.2% (95% CI: 1.4–9.3%) of the number of fatalities/ha as had the first searches of 1998−2014 when turbines were present and operable.

Where no infrastructure remained, I found fatalities of 8 species where the rate of detections of first searches of 1998−2014 would predict 11, as well as 0 of 8 predicted golden eagle fatalities, 0 of 4 American kestrels, 0 of 10 European starlings (Sturnus vulgaris), 0 of 28 rock pigeons, 1 of 7 American barn owls, 1 of 8 western meadowlarks, and 1 of 22 red-tailed hawks. Fatalities/ha were too few relative to the area I searched to support similar comparisons for horned larks (Eremophila alpestris), burrowing owls, and most other species that had been found in first searches from 1998 to 2014. Where infrastructure remained after turbines were removed, and applying the same assumption as above, I found 1 of 2.4 golden eagles, 0 of 9 red-tailed hawks, 1 of 4 burrowing owls, 2 of 2 American barn owls, 2 of 10 rock pigeons, and 0 of 4 western meadowlarks.

First searches from 1998 to 2014 averaged 20.1 turbine addresses (n = 16,137 searches) per fatality discovery, whereas subsequent periodic searches averaged 33.4 turbine addresses (n = 234,747 searches) per fatality discovery. First-search fatality detections composed 10.2% of total fatality detections from only 6.4% of all turbine searches. Compared to scent-detection dogs, which found disproportionately more bats and songbirds < 32 g body mass, humans searching at 2-day intervals found disproportionately more small birds, and humans searching at 5-day intervals found disproportionately more fatalities of mid-sized species such as burrowing owls (Figure 4). First searches before and after turbine removals found disproportionately more fatalities of species ≥ 1 kg (Figure 4).

Body sizes of fatalities found in first searches were unequally distributed before and after turbines were removed (Figure 2). About the same proportions of mid-sized birds were found before and after turbine removals, but a larger proportion of birds ≥ 1 kg was found before turbine removals, and a larger proportion of birds < 100 g was found after turbine removals. Therefore, fatality rates before and after removals would be more comparable by adjusting fatality findings for proportions of fatalities found as functions of body mass, D (see Section 2.3 Bias Identification and Adjustment). Where infrastructure remained after turbine removals, estimated fatalities/ha of all birds based on first searches were 1.924 (95% CI: 0.4293–3.4170) before turbine removals and 0.2966 (95% CI: 0.0000–0.7548) after turbine removals (

Table 5. Estimates of fatalities/ha adjusted for the effects of body mass before and after turbine removals where infrastructure remained after removals, Altamont Pass Wind Resource Area, California, 1998–2019.

Species	Estimated Fatalities/ha with Infrastructure
	Before Turbine Removals		After Turbine Removals
	$\overline{x}$	95% CI	$\overline{x}$	95% CI
Mallard (Anas platyrhynchos)	0.0178	0.0018–0.0336
Golden eagle (Aquila chrysaetos)	0.0178	0.0019–0.0338	0.0044	0.0000–20.013
Red-tailed hawk (Buteo jamaicensis)	0.0110	0.0000–0.0245
Buteo	0.0951	0.0436–0.1453
Prairie falcon (Falco mexicanus)	0.0063	0.0000–0.0159
Raptor	0.0219	0.0006–0.0431
American avocet (Recurvirostra Americana)	0.0117	0.0000–0.0346
Ring-billed gull (Larus delawarensis)	0.0249	0.0000–0.0572
Gull (Laridae)			0.0081	0.0000–0.0241
American barn owl (Tyto furcata)	0.0266	0.0000–0.0570	0.0386	0.0000–0.0926
Great horned owl (Bubo virginianus)	0.0065	0.0000–0.0167
Burrowing owl (Athene cunicularia)	0.0833	0.0216–0.1450	0.0462	0.0000–0.1369
White-throated swift (Aeronautes saxatalis)	0.0281	0.0000–0.0687
Northern flicker (Colaptes auratus)	0.0041	0.0000–0.0122
Loggerhead shrike (Lanius ludovicianus)	0.0347	0.0000–0.1026
American crow (Corvus brachyrhynchos)	0.0057	0.0000–0.0167	0.0067	0.0000–0.0198
Common raven (Corvus corax)	0.0268	0.0034–0.0499
European starling (Sturnus vulgaris)	0.0123	0.0000–0.0365
American pipit (Anthus rubescens)	0.8276	0.4260–1.2297	0.1159	0.0000–0.2859
Red-winged blackbird (Agelaius phoeniceus)	0.1822	0.0455–0.3190
Brewer’s blackbird (Euphagus cyanocephalus)	0.0178	0.0000–0.0441
Blackbird (Icteridae)	0.0576	0.0000–0.1238
House finch (Carpodacus mexicanus)	0.0990	0.0000–0.2246
Small bird	0.2849	0.0349–0.5349	0.0766	0.0000–0.1823
Large bird	0.0203	0.0000–0.0475
All birds	1.9240	0.4293–3.4170	0.2966	0.0000–0.7548

). Where no infrastructure remained after turbine removals, estimated fatalities/ha of all birds based on first searches were 2.1122 (95% CI: 0.6137–3.6073) before turbine removals and 0.0763 (95% CI: 0–0.2226) after turbine removals (

Table 6. Estimates of fatalities/ha adjusted for the effects of body mass before and after turbine removals where no infrastructure remained after removals, Altamont Pass Wind Resource Area, California, 1998–2019.

Species	Estimated Fatalities/ha Without Infrastructure
	Before Turbine Removals		After Turbine Removals
	$\overline{x}$	95% CI	$\overline{x}$	95% CI
Mexican free-tailed bat (Tadarida brasiliensis)	0.0236	0.0000–0.0698
Mallard (Anas platyrhynchos)	0.0127	0.0020–0.0233
Ring-necked duck (Aythya collaris)	0.0008	0.0000–0.0023
Cattle egret (Ardea ibis)	0.0012	0.0000–0.0035
Turkey vulture (Cathartes aura)	0.0021	0.0000–0.0054
Golden eagle (Aquila chrysaetos)	0.0265	0.0105–0.0426
Red-tailed hawk (Buteo jamaicensis)	0.0190	0.0030–0.0347
Ferruginous hawk (Buteo regalis)	0.0060	0.0000–0.0130
Buteo	0.1043	0.0672–0.1390	0.0133	0.0000–0.0394
American kestrel (Falco sparverius)	0.0146	0.0000–0.0406
Prairie falcon (Falco mexicanus)	0.0042	0.0000–0.0093
Raptor	0.0209	0.0049–0.0367
Ring-billed gull (Larus delawarensis)	0.0283	0.0074–0.0491
California gull (Larus californicus)	0.0068	0.0000–0.0181
Gull (Laridae)	0.0009	0.0000–0.0028	0.0069	0.0000–0.0169
Rock pigeon (Columba livea)	0.0028	0.0000–0.0084
Mourning dove (Zenaida macroura)	0.0026	0.0000–0.0063
American barn owl (Tyto furcata)	0.0417	0.0151–0.0680	0.0021	0.0000–0.0063
Great horned owl (Bubo virginianus)	0.0012	0.0000–0.0036
Burrowing owl (Athene cunicularia)	0.0086	0.0000–0.0218	0.0099	0.0000–0.0294
Northern flicker (Colaptes auratus)	0.0632	0.0009–0.1256	0.0045	0.0000–0.0134
Loggerhead shrike (Lanius ludovicianus)	0.0056	0.0000–0.0165
American crow (Corvus brachyrhynchos)	0.0088	0.0000–0.0213	0.0026	0.0000–0.0078
Common raven (Corvus corax)	0.0466	0.0216–0.0707
Horned lark (Eremophila alpestris)	0.0060	0.0000–0.0178
Cliff swallow (Hirundo pyrrhonota)	0.0641	0.0000–0.1537
Mountain bluebird (Sialia currucoides)			0.0107	0.0000–0.0317
European starling (Sturnus vulgaris)	0.0277	0.0000–0.0593
American pipit (Anthus rubescens)	1.1098	0.4623–1.7579
Savannah sparrow (Passerculus sandwichensis)	0.0191	0.0000–0.0455
Red-winged blackbird (Agelaius phoeniceus)	0.1672	0.0738–0.2667	0.0097	0.0000–0.0287
Western meadowlark (Sturnella neglecta)			0.0165	0.0000–0.0490
Brewer’s blackbird (Euphagus cyanocephalus)	0.0176	0.0000–0.0443
House finch (Carpodacus mexicanus)	0.0533	0.0000–0.1208
Small bird	0.1681	0.0447–0.2916
Medium bird	0.0372	0.0041–0.0701
Large bird	0.0126	0.0019–0.0232
All birds	2.1122	0.6137–3.6073	0.0763	0.0000–0.2226

). Infrastructure alone accounted for 8.2% (95% CI: 0–15.8%) of bird mortality. Therefore, wind turbines alone accounted for 88.2% (95% CI: 78–100%) of bird mortality, and both wind turbines and their ancillary infrastructure accounted for 96.4% (95% CI: 93.8–100%) of bird mortality. Background mortality was 3.6% (95% CI: 0–6.2%).

4. Discussion

Searches for background mortality at wind projects revealed that wind turbines caused most of the fatalities found during monitoring of operable wind turbines. Ancillary infrastructure in the APWRA caused more than double the fatalities as background mortality, which was estimated to compose only 3.6% of total bird mortality, and it caused none of the bat mortality. This background mortality estimate for birds was potentially biased high because of contamination from wind turbines that continued to operate ≥400 m from the vacant turbine pads that I searched. On vacant turbine pads in the APWRA, I detected 0.0194 (95% CI: 0.0035–0.0352) bird fatalities/ha, which was 3.5 times more fatalities/ha than found at sites located much farther from wind turbines.

Background mortality averaged 0.0055 fatalities/ha among both the literature sources and my own study areas outside the APWRA. The 95% confidence intervals based on these two data sets were nearly equal, indicating strong consistency. Although it is possible that the much lower background mortality located far from wind turbines reflected the effects of turbine-caused contamination of the APWRA’s vacant turbine addresses, it is also possible that it reflects a difference in bird abundance and behavior between a wind resource area and less windy places. If the former explanation was true, then my background fatality searches in the APWRA were insufficiently distant from wind turbines to measure true background mortality. To measure true background mortality, background fatality searches need to be conducted beyond the distances to which scavengers carry carcass remains and mortally injured animals travel from the collision sites before perishing. Such distances are likely 5 km, 10 km, or even farther from collision sources.

However, as best as I could determine from [39], ICF’s first background mortality searches in fall 2014 totaled 178 ha at vacant addresses and turbine-free plots and 178 ha at turbine-occupied addresses in the APWRA. Their first searches yielded 4 bird fatalities < 90 days since death (1 large bird, 2 gulls, 1 American kestrel) at vacant turbine addresses, or 0.0225 fatalities/ha, and 11 bird fatalities < 90 days since death (2 golden eagles, 1 ferruginous hawk, 2 large birds, 1 burrowing owl, 1 dove, 1 rock pigeon, 1 common raven, 1 western meadowlark, 1 small bird) at turbine-occupied addresses, or 0.0618 fatalities/ha. In another study that overlapped the study period of their background mortality study, the same searchers struggled to accurately identify species and estimate time since death, probably because of their long 39-day search interval [9]. Assuming the birds unidentified to species in ICF’s background fatality searches had likewise been dead longer than ICF had estimated [39], only the American kestrel should have been counted among vacant turbine addresses. Counting only the American kestrel, background mortality would have been 0.0056 fatalities/ha, which was nearly exactly the rate of 0.0055 fatalities/ha found at sites far from wind turbines (Table 2 and Table 3). Applying the same assumption to the turbine-occupied addresses, the number of fatalities < 90 days since death found during ICF’s first searches would have been 7, or 0.0393 fatalities/ha. This latter rate was twice the 0.0194 fatalities/ha that I found at vacant turbine addresses where infrastructure had been removed along with the turbines. ICF’s [39] higher rate was understandable, given the greater collision hazards posed by the inoperable wind turbines that remained within their search areas. In another study, inoperable wind turbines in the APWRA killed birds at rates that did not differ significantly from operable wind turbines [42].

Background mortality measured for birds in this study averaged lower than the 20% of wind turbine-caused fatalities implied by [3] and lower than the percentages claimed by others. For all birds as a group, I measured background mortality to have been 3.6% of mortality caused by wind turbines. To arrive at useful species-specific adjustments for background mortality, either human searchers would need to search for fatalities over many thousands of hectares where anthropogenic causes of fatalities are minimal, or a smaller but nevertheless large area might be searched using scent-detection dogs. Background mortality of bats was not measurable across 6658 ha of searches, but human detection of bat fatalities is very low [12,54]. The only empirically founded adjustment available for background mortality is the 3.6% estimate from the APWRA, but this estimate was based on 0.0194 fatalities/ha—a rate that was 3.5 times higher than the average of 0.0055 fatalities/ha measured at sites far from wind turbines. True background mortality likely lies somewhere between 1% (3.6% ÷ 3.5-fold difference between the AWPRA and sites far from turbines) and 3.6% of birds. Adjusting fatality estimates for this level of background mortality would probably be unwarranted in light of multiple larger sources of bias and uncertainty in fatality estimation at wind and solar projects.

Although I estimated fatalities/ha for individual species, I refrained from estimating background mortality or mortality caused by turbines or infrastructure at the species level. Sample sizes were too small for partitioning out fatality rates to infer cause of death. Much larger search efforts would have been needed for species-specific estimation of background mortality—perhaps 10 times the search effort of this study.

A leading reason for concern over background mortality was the frequent inability to definitively assign cause of death to fatalities found during fatality monitoring. However, even for many of the birds and bats that most certainly died from collision with wind turbines, underlying circumstances might have predisposed them to collision with wind turbines. Birds and bats colliding with wind turbines could have earlier been debilitated by injuries or illnesses that originated naturally or anthropogenically. Birds injured by electrocution or collision with transmission or distribution lines, and birds that survived gunshot injuries, might suffer loss of flight control or sensory perception. Direct or trophic ingestion of pesticides, lead, or other toxicants might also debilitate birds and bats. In particular, the deployment of anticoagulant poisons to reduce raptor prey populations—a common mitigation measure implemented at new wind projects—might increase collision risk of the very raptors the mitigation measure was supposed to benefit. Starvation might contribute to some collisions, as might the added energetic costs of birds or bats having to navigate increasingly hazardous airspace. Some wind turbine collision victims might sometimes survive long enough with debilitating injuries to collide with another wind turbine far from the earlier collision site. Regardless, animals ultimately killed by collision with an anthropogenic structure would have lived longer had the structure not been installed in the animal’s airspace. All but a very few of the fatalities found around wind turbine or solar facilities died prematurely because of the facilities.

Management Implications

Renewable energy infrastructure causes the majority of bird and bat fatalities found in fatality searches, in the case of the APWRA’s wind turbines leaving the only supportable adjustment for background mortality to be ≤3.6%, which is applicable only to all birds as a group. Short of special circumstances or new data, reports of fatality monitoring would better inform readers by not repeating the common claim that fatality estimates were biased high by background mortality or that background mortality composed a substantial portion of estimated mortality. Considering the large study effort that would be needed to generate diminutive, species-specific adjustment factors for background mortality, resources would be better directed towards making other improvements in fatality monitoring. Also, the 8.2% of mortality attributable to ancillary infrastructure in this study suggests that new wind projects or solar projects can prevent many collision fatalities by undergrounding power lines and by foregoing meteorological towers and security fences. Where wind turbines or PV solar projects are decommissioned, collision fatalities will continue until the ancillary infrastructure is also removed along with the wind turbines or solar panels.