Repurposing open‐source data from weather radars to reduce the costs of aerial waterbird surveys

Abstract

Aerial counts are the primary means of monitoring waterbird populations. A valid population assessment requires a significant proportion of the population to be surveyed. For broad‐ranging species, this requires costly reconnaissance flights and surveys over large areas of potential habitat. Here, we assess whether free, autonomously collected weather radar reflectivity data can identify waterbird aggregations over broad areas of potential habitat, thereby providing a low‐cost means of directing the timing and location of aerial survey counts. Our study was undertaken over a large flood plain known to be a key seasonal nesting area of the Magpie Goose (Anseranas semipalmata). A standardized aerial survey technique was employed, firstly using reconnaissance flights to determine when the birds were nesting and then 400‐m wide transects 3 km apart over approximately 1100 km, visually counting the geese. Radar reflectivity data from an operational weather radar in Darwin, Australia, 2 weeks prior to and during the aerial survey, were processed to link averaged radar reflectivity to terrestrial habitat at a spatial polar resolution of 250 m × 0.5°. A binary classification approach evaluated the accuracy, precision, sensitivity and specificity of reflectivity data in identifying areas where the aerial surveys recorded geese. Aerial surveys of Magpie Geese are most effective when the geese are nesting, which is usually identified by reconnaissance flights. Here, we show nesting correlated with a decline in radar reflectivity and a concentration of hotspots. By achieving reasonable accuracy and precision (77% and 67%, respectively), the weather radar reflectivity identified high aggregations of Magpie Geese and demonstrated high efficacy when identifying areas where Magpie Geese did not occur (specificity, 94%). We conclude that using weather radar data to guide aerial survey timing would reduce reconnaissance flight needs, and enable aerial surveys to focus on areas with a large proportion of the target waterbird population.

Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements

“…However, unethically or irresponsibly developed AI may eventually cause more harm, which can be observed in a few high-profle cases beyond the context of weather and climate [187,188]. Te benefts of AI in terms of environmental sustainability are undoubtedly evident, such as allowing automated monitoring of the ecosystems to support accountability for climate justice, for example, monitoring changes in land cover for the detection of deforestation [189][190][191], counting endangered species populations in very high-resolution satellite data [192,193], and tracking bird populations in radar data [194][195][196]. Trough automated analysis of retrospective and real-time datasets, relevant stakeholders can continue to monitor environmental trends and provide timely and efective responses.…”