Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results.
Accurate predictions of the abundance of migrating birds are important to avoid aerial conflicts of birds, for example, with aviation or wind power installations. Here we develop a predictive model, using bird migration intensity extracted from operational weather data. We compare baseline phenological models to models incorporating both local and remote weather conditions using an ensemble approach. Single models are compared to ensemble models (average prediction of top 10 models). The models were evaluated by omitting single years from our 10‐year dataset. In general, we find that wind conditions, in addition to seasonal and diurnal dynamics, are key for accurate predictions. The spring and fall migratory seasons differ, both with respect to the selected environmental variables and the contribution of the environmental model compared to the phenological model. In fall, the accumulation of migrants due to strong headwinds is an important predictor of migration. Because of the lower daily variation in migration intensity in spring, the phenological model performs better compared to fall. In fall, weather conditions contribute more to accurate predictions of migration intensity than in spring. Overall, the ensemble approach produces more accurate predictions outperforming specific environmental models. We therefore recommend that ensemble models be used in operational settings such as flight planning to reduce bird aircraft collisions during intense bird migration.
- NSF-PAR ID:
- 10372675
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecological Solutions and Evidence
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2688-8319
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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