DOI: 10.5061/dryad.8gtht76x0
title: Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset

description: 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.
description: # Data from: Forecasting nocturnal bird migration for dynamic
 aeroconservation: the value of short-term dataset
 [https://doi.org/10.5061/dryad.8gtht76x0](https://doi.org/10.5061/dryad.8gtht76x0) The two datasets contain seasonal (spring and autumn) hourly measurements of migration intensity derived from the bird tracking radar data (Robin Radar, 3D-fixed) located in the Luchterduinen wind farm (52.25 N, 4.10 E), 23 km off the western Dutch coast and the corresponding weather variables extracted from the European Centre for Medium-Range Weather Forecast ERA5 reanalysis dataset (Hersbach et al., 2020) for a grid cell with centroid in 52.25 N 4.00 E or their derivatives. The dataset also contains calculated hourly proxies for the phenology of bird migration. Please see the connected paper and the supplementary information for more general information and details on how these variables were derived. Raw radar data, processed by the radar software, is collected by Rijkswaterstaat (the Dutch Ministry of Infrastructure and Water Management) and a copy is stored by the University of Amsterdam. For more information about the data, contact the authors of this paper. The data matrix described below was used to train the spring and autumn forecast model, which predicts the hourly intensity of nocturnal bird migration in the Dutch southern North Sea. Measurements of migration intensity are not available (MTR column set to NA) if the radar was off due to maintenance or technical failure or when automated clutter filtering activity in the radar software was too high for the data to be considered reliable (e.g. moments of high sea state). ## Description of the data and file structure The following tables can be combined to understand the variables in datasets, as well as their locations and units. The related manuscript can give more details about departure locations and specific calculations of variables. The tables contain multiple years of data for the spring (2019-2023) and autumn (2019-2022) migration seasons. Table 1. Glossary of abbreviations in data tables. | **Abbreviation** | **Description** | | :--------------- | :--------------------------------------- | | acc | accumulation | | acc\_diff | (nightly) difference in accumulation | | tp or P | total precipitation | | wa | wind assistance | | t | temperature | | msl | Mean sea level pressure | | L | Radar location (Luchterduinen) | | UK | United Kingdom | | F | France | | NW | Nort-west Germany | | NN | North Netherlands | | D | Denmark | | delta | (nightly) difference in weather variable | | Hour\_p | Proxy for diurnal phenology | | Year\_p | Proxy for seasonal phenology |  Table 2. Variables available in data tables. | **Description** | **Unit** | **Season** | **Location** | | :------------------------------------------------------------ | :--------- | :--------- | :------------------------------- | | Time (UTC) | h | Both | - | | Migration traffic rate (MTR) | Birds/km/h | Both | Radar location | | Wind assistance toward W | m/s | Autumn | Radar and NW Germany | | Wind assistance  toward SW | m/s | Autumn | Radar, N Netherlands and Denmark | | Wind assistance toward E | m/s | Spring | Radar and the UK | | Wind assistance toward NE | m/s | Spring | Radar and NW France | | Total precipitation | mm | Both | Radar and departure locations | | Temperature | °C | Both | Radar and departure locations | | Mean sea level pressure | hPa | Both | Radar and departure locations | | The nightly difference in mean sea level pressure | hPa | Both | Departure locations | | Accumulation due to wind assistance | - | Both | Departure locations | | The nightly difference in accumulation due to wind assistance | - | Both | Departure locations | | Accumulation due to total precipitation | - | Both | Departure locations | | The nightly difference in accumulation due to precipitation | - | Both | Departure locations | | Diurnal phenology | - | Both | - | | Seasonal phenology | - | Both | - |