Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol

Abstract

Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models (glm)) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.

Influencing Activity of Bats by Dimly Lighting Wind Turbine Surfaces with Ultraviolet Light

“…To date, the most common metric of efficacy for fatality reduction tests has been to compare the number of dead bats found beneath wind turbines following nights with and without applied treatments (e.g., curtailment, acoustic deterrents [ 20 , 21 , 25 , 26 , 27 , 109 , 110 ]). This gold standard of counting carcasses to judge if a method works is fully justified and supported by robust statistical measures and laboratory techniques to enhance the quality, precision, and reliability of such data [ 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 ]. Had we relied on fatality ground searches as a metric of bat response to dim-UV treatment, we would not have been able to statistically test for treatment effects, by either attraction or deterrence, simply (and fortunately) due to the low sample size.…”

Accounting for the Fraction of Carcasses outside the Searched Area and the Estimation of Bird and Bat Fatalities at Wind Energy Facilities

“…Accounting for anisotropy introduces a considerable degree of statistical complexity along with more extensive data requirements. Perhaps the most straightforward and flexible way to account for anisotropy would be via Poisson regression of carcass counts on a coordinate grid, as discussed in Maurer et al (2020). The dwp package (version 1.0.3) does not include special functions for handling anisotropy.…”

“…After models with implausible extrapolations or worrisome instabilities have been filtered out, criteria like AICc that measure the quality of a model's fit strictly within the range of the data are useful for distinguishing among plausible models. AICc and other internal criteria are especially valuable when there are substantial unsearched areas within the search radius, as would be the case with road and pad searches (Maurer et al 2020) or with sites with forest or other thick vegetation, water, cliffs, poisonous snakes, or other features that render much of the area around turbines practically unsearchable. Predicting the proportion of carcasses in unsearched areas within the search radius is an interpolation problem rather than extrapolation, and the quality of the fit to the data within the search radius is directly relevant in a way that it is not when extrapolating beyond the search radius.…”

“…Of particular interest would be accounting for anisotropy, which is variability in carcass distribution that depends on direction, as might happen if a prevailing wind direction strongly affects carcass dispersion patterns. These anisotropic models are discussed by Maurer et al (2020) and not addressed here or in the software.…”

“…Interacting covariates that affect the actual distances that carcasses fly-like wind speed, turbine height, carcass size-present special difficulties that are difficult to model using the kinds of automated procedures used in most of the convenient dwp functions and are not implemented in the basic package functions. Savvy R users may be able to use the dwp functions as a basis for more elaborate analyses, including accounting for anisotropy (or carcass dispersion patterns that vary with direction from turbines) (Maurer et al 2020) or analyzing dependence of dispersion on other interacting variables.…”