Swallow and martin roosts detected on WSR in the Great Lakes region from 2000 to 2022

This dataset contains the results the following data processing steps:

1) Rendering of Weather Surveillance Radar data and using it as input to the machine learning model described in Cheng et al. (2020) that is capable of detecting and tracking swallow and martin roost signatures.

2) Manual screening of the model's results, labeling each true positive track according to the type and degree of contamination of the roost by other sources of non-biological scattering.

3) Grouping of overlapping tracks that can be considered as the same roost dispersal event.

5) Calculation of number of birds per detection, assuming a Purple Martin (Progne subis) radar cross-section (following the procedure proposed by Chilson et al 2012), and summarizing counts across all radar sweeps.

These procedures have been described in detail in the following publication: Belotti, M.C.T.D., Deng, Y., Zhao, W., Simons, V.F., Cheng, Z., Perez, G., Tielens, E., Maji, S., Sheldon, D., Kelly, J.F. and Horton, K.G. (2023), Long-term analysis of persistence and size of swallow and martin roosts in the US Great Lakes. Remote Sens Ecol Conserv, 9: 469-482. https://doi.org/10.1002/rse2.323

Code for the analysis of these data can be found here: https://gitlab.com/mariabelotti/prjct_number_of_birds

References:

Cheng, Z., Gabriel, S., Bhambhani, P., Sheldon, D., Maji, S., Laughlin, A., & Winkler, D. (2020). Detecting and Tracking Communal Bird Roosts in Weather Radar Data. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 378–385. https://doi.org/10.1609/aaai.v34i01.5373

Chilson, P. B., W. F. Frick, P. M. Stepanian, J. R. Shipley, T. H. Kunz, and J. F. Kelly. (2012). Estimating animal densities in the aerosphere using weather radar: To Z or not to Z? Ecosphere, 3(8):72. http://dx.doi.org/10.1890/ES12-00027.1