Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset

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 urgent development of conservation measures that rely on
existing data, even when it does not have long temporal coverage. Here, we
use five 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. Synthesis and applications: Near-term forecasts of
migration fluxes evaluated against measurements can be used to define
curtailment thresholds for offshore wind energy. We show that to minimize
collision risk for 50% of migrants, if predicted correctly, curtailments
should be applied during 18 hours in spring and 26 in autumn in the focal
year of model assessments, resulting in an estimated annual wind energy
loss of 0.12%. Drawing from the Dutch curtailment framework, which
pioneered the 'international first' offshore curtailment, we
argue that using forecasts developed from limited temporal datasets
alongside expert insight and data-driven policies can expedite
conservation efforts in a rapidly changing world. This approach is
particularly valuable in light of increasing interannual variability in
weather conditions.