Avian Monitoring and Risk Assessment at the Tehachapi Pass Wind Resource Area; Period of Performance: October 2, 1996--May 27, 1998

Anderson, R.; Neumann, Norman F.; Tom, Joe G.; Erickson, Wallace P.; Strickland, M. Dale; Bourassa, M.; Bay, Kimberly; Sernka, Karyn J.

doi:10.2172/15009631

Cited by 15 publications

(15 citation statements)

References 3 publications

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“…Kerlinger 2002; Anderson et al 2004;Kerns et al 2005;Jain et al 2007), which limits and may compromise inference; (2) based on a a priori distribution (exponential distribution) (e.g. Jonhson et al 2003;Erickson et al 2004;Fiedler 2004;Huso 2010), or (3) using estimators not accounting for censored observations (e.g.…”

Section: Discussionmentioning

confidence: 99%

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

et al. 2012

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In monitoring studies at wind farms, the estimation of bird and bat mortality caused by collision must take into account carcass removal by scavengers or decomposition. In this paper we propose the use of survival analysis techniques to model the time of carcass removal. The proposed method is applied to data collected in ten Portuguese wind farms. We present and compare results obtained from semiparametric and parametric models assuming four main competing lifetime distributions (exponential, Weibull, log-logistic and log-normal). Both homogeneous parametric models and accelerated failure time models were used. The fitted models enabled the estimation of the carcass persistence rates and the calculation of a scavenging Author's personal copy Environ Ecol Stat correction factor for avian mortality estimation. Additionally, we discuss the impact that the distributional assumption can have on parameter estimation. The proposed methodology integrates the survival probability estimation problem with the analysis of covariate effects. Estimation is based on the most suitable model while simultaneously accounting for censored observations, diminishing scavenging rate estimation bias. Additionally, the method establishes a standardized statistical procedure for the analysis of carcass removal time in subsequent studies.

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Section: Discussionmentioning

confidence: 99%

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

et al. 2012

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“…Searcher Detection I collected data on searcher detection rates from Orloff and Flannery (1992), Kerlinger et al (2000), Johnson et al (2002), Young et al (2003), Anderson et al (2004Anderson et al ( , 2005, Table 1. Unpublished reports of bird collision studies at wind farms in the United States, from which I used data on reported searcher detection and scavenger removal rates.…”

Section: Methodsmentioning

confidence: 99%

Estimating Wind Turbine‐Caused Bird Mortality

Smallwood¹

2007

J Wildl Manag

169

235

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Mortality estimates are needed of birds and bats killed by wind turbines because wind power generation is rapidly expanding worldwide. A mortality estimate is based on the number of fatalities assumed caused by wind turbines and found during periodic searches, plus the estimated number not found. The 2 most commonly used estimators adjust mortality estimates by rates of searcher detection and scavenger removal of carcasses. However, searcher detection trials can be biased by the species used in the trial, the number volitionally placed for a given fatality search, and the disposition of the carcass on the ground. Scavenger removal trials can be biased by the metric representing removal rate, the number of carcasses placed at once, the duration of the trial, species used, whether carcasses were frozen, whether carcasses included injuries consistent with wind turbine collisions, season, distance from the wind turbines, and general location. I summarized searcher detection rates among reported trials, and I developed models to predict the proportion of carcasses remaining since the last fatality search. The summaries I present can be used to adjust previous and future estimates of mortality to improve comparability. I also identify research directions to better understand these and other adjustments needed to compare mortality estimates among wind farms.

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“…Wind turbine-caused fatality rates were recently estimated at 2,710 (SE 5 11.848) birds per year in the APWRA, including 1,127 (SE 5 1.547) raptors per year (Smallwood and Thelander 2008). As a result of these high fatality rates, bird mortality has been investigated at other wind farms throughout North America, and bird behaviors and activity levels have been investigated at some of these (Janss and Clave 2000;Kerlinger 2000;Anderson et al 2004Anderson et al , 2005Hoover and Morrison 2005). These investigations attest to the importance attributed to bird behaviors and activity levels in relation to bird collisions with wind turbines.…”

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Influence of Behavior on Bird Mortality in Wind Energy Developments

2009

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As wind power generation is rapidly expanding worldwide, there is a need to understand whether and how preconstruction surveys can be used to predict impacts and to place turbines to minimize impacts to birds. Wind turbines in the 165‐km2 Altamont Pass Wind Resource Area (APWRA), California, USA, cause thousands of bird fatalities annually, including hundreds of raptors. To test whether avian fatality rates related to rates of utilization and specific behaviors within the APWRA, from March 1998 to April 2000 we performed 1,959 30‐minute behavior observation sessions (360° visual scans using binoculars) among 28 nonoverlapping plots varying from 23 ha to 165 ha in area and including 10–67 turbines per plot, totaling 1,165 turbines. Activity levels were highly seasonal and species specific. Only 1% of perch time was on towers of operating turbines, but 22% was on towers of turbines broken, missing, or not operating. Of those species that most often flew through the rotor zone, fatality rates were high for some (e.g., 0.357 deaths/megawatt of rated capacity [MW]/yr for red‐tailed hawk [Buteo jamaicensis] and 0.522 deaths/MW/yr for American kestrel [Falco sparverius]) and low for others (e.g., 0.060 deaths/MW/yr for common raven [Corvus corax] and 0.012 deaths/MW/yr for turkey vulture [Cathartes aura]), indicating specific behaviors or visual acuity differentiated these species by susceptibility to collision. Fatality rates did not correlate with utilization rates measured among wind turbine rows or plots for any species except burrowing owl (Athene cunicularia) and mallard (Anas platyrhynchos). However, mean monthly fatality rates of red‐tailed hawks increased with mean monthly utilization rates (r2 = 0.67) and especially with mean monthly flights through turbine rows (r2 = 0.92). Fatality rates increased linearly with rates of utilization (r2 = 0.99) and flights near rotor zones (r2 = 1.00) for large raptor species and with rates of perching (r2 = 0.13) and close flights (r2 = 0.77) for small non‐raptor species. Fatalities could be minimized or reduced by shutting down turbines during ≥1 season or in very strong winds or by leaving sufficiently large areas within a wind farm free of wind turbines to enable safer foraging and travel by birds.

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Avian Monitoring and Risk Assessment at the Tehachapi Pass Wind Resource Area; Period of Performance: October 2, 1996--May 27, 1998

Abstract: UL 18. NUMBER OF PAGES 19b. TELEPONE NUMBER (Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18

Cited by 15 publications

References 3 publications

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

Estimating Wind Turbine‐Caused Bird Mortality

Influence of Behavior on Bird Mortality in Wind Energy Developments

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