Strategies for Mitigating Impacts to Birds and Bats from Offshore Wind Energy Development: Available Evidence and Data Gaps
Bibliographic record
Abstract
SUMMARY A diversity of approaches exist for mitigating (e.g., avoiding, minimizing, or compensating for) the effects of offshore wind energy (OSW) development on birds and bats, but little is known about the effectiveness of many of these approaches. To address this knowledge gap, we reviewed the scientific and gray literature to evaluate the evidence base for potential bird- and bat-related mitigation approaches for OSW, including studies from other industries where relevant (e.g., terrestrial wind energy, offshore oil and gas industry). Of a total of 219 mitigation approaches, most focused on minimization, with far fewer addressing avoidance or compensation. Sixty-five percent had no evidence of testing. Of the 76 mitigation approaches that were field tested or implemented, we found evidence of effectiveness for only 40 approaches, of which only 10 were specific to the OSW sector. Consequently, a majority of all the mitigation approaches for birds (82%) and bats (89%) lacked any evidence of effectiveness. For birds, minimization approaches related to lighting reduction were the most tested and effective methods for reducing maladaptive attraction and collisions. For bats, minimization approaches involving adjustments to turbine operations (e.g., curtailment of turbine blades) were most tested and effective methods for reducing collisions. Given the limited evidence of effectiveness for most mitigation approaches, assuming their success systematically underestimating project-level and cumulative impacts, underscoring the need to prioritize avoidance, consistent with the mitigation hierarchy, and rigorously test mitigation approaches for birds and bats in offshore environments. RÉSUMÉ Il existe une diversité d’approches pour atténuer (e.g. éviter, minimiser ou compenser) les effets du développement de l’ l’Énergie Éolienne Extracôtière (EEE) sur les oiseaux et les chauves-souris, mais l’efficacité de nombreuses de ces approches est encore peu connue. Pour combler ces lacunes, nous avons passé en revue la littérature scientifique et la littérature grise afin d’évaluer les données probantes concernant les mesures d’atténuation potentielle pour l’EEE et les oiseaux et les chauves-souris,, inclus des études d’autres industries lorsque cela était pertinent (e.g., l’énergie éolienne terrestre, l’industrie pétrolière et gazière extracôtière). Sur un total de 219 approches d’atténuation pertinentes pour l’EEE, la plupart des approches se concentraient sur la minimisation, avec beaucoup moins d’approches portant sur l’évitement ou la compensation. Soixante-et-un pourcent des approches proposées ne semblent pas avoir été testées. Parmi les 76 approches d’atténuation qui ont été testées sur le terrain ou mises en œuvre, nous avons trouvé des preuves de leur efficacité pour seulement 40 approches, dont seulement 10 étaient spécifiques àle secteur de l’EEE. Par conséquent, la majorité des approches d’atténuation pour les oiseaux (82 %) et les chauves-souris (89%) ne disposaient d’aucune preuve d’efficacité. Chez les oiseaux, les approches de minimisation liées à la réduction de l’éclairage étaient les méthodes les plus couramment testées et les plus efficaces pour réduire l’attraction et les collisions. Chez les chauves-souris, les approches de minimisation impliquant la modification du fonctionnement des turbines (e.g., réduction de la vitesse de rotation ou mise en drapeau des hélices) étaient les méthodes les plus couramment testées et les plus efficaces pour réduire les collisions. Compte tenu des preuves limitées de l’efficacité de la plupart des approches d’atténuation, le fait de présumer de leur succès conduit à une sous-estimation systématique des impacts à l’échelle des projets et des impacts cumulatifs, ce qui souligne la nécessité de privilégier l’évitement, conformément à la hiérarchie d’atténuation, et de tester rigoureusement les mesures d’atténuation pour les oiseaux et les chauves-souris en milieux offshore. IMPLICATIONS FOR MANAGERS Based on our literature review of potential mitigation approaches for birds and bats in relation to offshore wind development, most approaches remain untested or lack evidence of effectiveness (bats – 89%, birds – 82%). Most minimization approaches have primarily been tested in the terrestrial context, leaving important questions regarding the transferability of these results to the offshore environment. Given the limited evidence of effectiveness for most mitigation approaches, assuming their success systematically underestimates project-level and cumulative impacts, underscoring the need to prioritize avoidance and rigorously test mitigation approaches for birds and bats in offshore environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".