Limitations, lack of standardization, and recommended best practices in studies of renewable energy effects on birds and bats
Bibliographic record
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 We evaluated 628 peer-reviewed publications, unpublished reports, and citations, and we analyzed data from 525 of these sources (203 facilities: 193 wind and 10 solar) in the United States and Canada to determine the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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".