Limitations, lack of standardization, and recommended best practices in studies of renewable energy effects on birds and bats

Abstract

Increasing global energy demand is fostering the development of renewable energy as an alternative to fossil fuels. However, renewable energy facilities may adversely affect wildlife. Facility siting guidelines recommend or require project developers complete pre‐ and postconstruction wildlife surveys to predict risk and estimate effects of proposed projects. Despite this, there are no published studies that have quantified the types of surveys used or how survey types are standardized within and across facilities. We evaluated 628 peer‐reviewed publications, unpublished reports, and citations, and we analyzed data from 525 of these sources (203 facilities: 193 wind and 10 solar) in the United States and Canada to determine the frequency of pre‐ and postconstruction surveys and whether that frequency changed over time; frequency of studies explicitly designed to allow before‐after or impact‐control analyses; and what types of survey data were collected during pre‐ and postconstruction periods and how those data types were standardized across periods and among facilities. Within our data set, postconstruction monitoring for wildlife fatalities and habitat use was a standard practice (n = 446 reports), but preconstruction estimation of baseline wildlife habitat use and mortality was less frequently reported (n = 84). Only 22% (n = 45) of the 203 facilities provided data from both pre‐ and postconstruction, and 29% (n = 59) had experimental study designs. Of 108 facilities at which habitat‐use surveys were conducted, only 3% estimated of detection probability. Thus, the available data generally preclude comparison of biological data across construction periods and among facilities. Use of experimental study designs and following similar field protocols would improve the knowledge of how renewable energy affects wildlife. Article Impact Statement Many surveys at wind and solar facilities provide limited information on wildlife use and fatality rates.

Estimation of spatiotemporal trends in bat abundance from mortality data collected at wind turbines

“…Our aim was to leverage consistent data collection and apply consistent statistical treatment among turbines (Conkling et al. 2020) to estimate the magnitude of potential trends, not to compare estimates of absolute mortality.…”

Unpacking the eagle collision risk model: practical guidance for wind energy

“…The current prior exposure distribution was built using data from nine wind facilities (New et al 2015), and wind energy developers have stated that the facilities used to build the current prior exposure distribution represent a limited and biased sample (Speerschneider 2018). Historically, a lack of standardization among monitoring protocols at wind facilities has limited the ability to analyze greater amounts of data (Conkling et al 2020), and the Service has created standard criteria for data to be included in the development of priors (U.S. Fish and Wildlife Service 2013).…”

Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife

“…Such studies usually require an experimental design that allows comparison of monitoring data collected pre‐ and post‐construction or on‐ and off‐site (Conkling et al. ). In the case of direct mortality effects, design of field surveys requires estimation of detection and carcass removal rates (Huso , Huso and Dalthorp , Huso et al ).…”

“…The limited publicly available fatality data from solar energy facilities rarely include estimates adjusted by scavenger removal rates or searcher detection rates (Conkling et al. ). Thus, counts of dead roadrunners substantially undercount the actual number of fatalities.…”

“…At many renewable energy facilities, there have been environmental assessments to estimate pre‐construction abundance of wildlife populations and post‐construction numbers of fatalities (e.g., Conkling et al ). These numerical assessments use a newly developed suite of field‐based and statistical tools for collecting and interpreting survey data (e.g., Dalthorp et al.…”