Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking. We present a generalised semi‐automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from c. 20,000 to c. 250,000 birds, providing estimates of bird nests. Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil, etc.). Our semi‐automated approach was between three and eight times faster than manually counting nests from imagery at the same level of accuracy. This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open‐source (Google Earth Engine and R), and easy to apply to other surveys.

Drones are rapidly becoming a key part of the toolkit for a range of scientific disciplines, as well as a range of management and commercial applications. This presents a number of challenges in context of how drone use might impact nearby wildlife. Interactions between birds and drones naturally come to mind, since they share the airspace. This paper details initial findings on the interactions between drones and birds for a range of waterbird, passerine and raptor species, across of a range of scientific applications and natural environments. The primary aims of this paper are to provide guidance for those planning or undertaking drone monitoring exercises, as well as provide direction for future research into safe and effective monitoring with drones. Our study sites we all located within Australia and spanned a range of arid, semi-arid, dunefield, floodplain, wetland, woodland, forest, coastal heath and urban environments. We particularly focus on behavioral changes towards drones during breeding season, interactions with raptors, and effects on nesting birds in large colonies -three areas yet to be explored in published literature. In over 70 hours of flight, there were no incidents with birds. Although some aggressive behavior was encountered from solitary breeding birds. Several large breeding bird colonies were surveyed, and included in our observations is monitoring and counting of nests in a colony of over 200,000 Straw-necked Ibis, the largest drone-based bird monitoring exercise to date. In addition to providing observations of interactions with specific bird species, we recommend procedures for flight planning, safe flying and avoidance. This paper also provides a basis for a number of critical and emerging areas of research into bird-drone interactions, most notably, territorial breeding birds, safety around large raptors, and the effect of drones on the behaviour of birds in large breeding colonies.

Drones are rapidly becoming a key part of the toolkit for a range of scientific disciplines, as well as a range of management and commercial applications. This presents a number of challenges in context of how drone use might impact nearby wildlife. Interactions between birds and drones naturally come to mind, since they share the airspace. This paper details initial findings on the interactions between drones and birds for a range of waterbird, passerine and raptor species, across of a range of scientific applications and natural environments. The primary aims of this paper are to provide guidance for those planning or undertaking drone monitoring exercises, as well as provide direction for future research into safe and effective monitoring with drones. Our study sites we all located within Australia and spanned a range of arid, semi-arid, dunefield, floodplain, wetland, woodland, forest, coastal heath and urban environments. We particularly focus on behavioral changes towards drones during breeding season, interactions with raptors, and effects on nesting birds in large colonies-three areas yet to be explored in published literature. In over 70 hours of flight, there were no incidents with birds. Although some aggressive behavior was encountered from solitary breeding birds. Several large breeding bird colonies were surveyed, and included in our observations is monitoring and counting of nests in a colony of over 200,000 Straw-necked Ibis, the largest drone-based bird monitoring exercise to date. In addition to providing observations of interactions with specific bird species, we recommend procedures for flight planning, safe flying and avoidance. This paper also provides a basis for a number of critical and emerging areas of research into bird-drone interactions, most notably, territorial breeding birds, safety around large raptors, and the effect of drones on the behaviour of birds in large breeding colonies.

Drones are rapidly becoming part of environmental monitoring and management applications. They provide an opportunity to improve a number of activities related to monitoring population dynamics of aggregations of wildlife. Bird surveys using drones have attracted particular attention, with a range of potential metrics able to be derived from high resolution drone imagery. Whilst a number of papers have shown that drone-based data can be used to effectively and accurately count and monitor features in bird colonies, the use of drone-derived data in real management and monitoring applications remains rare. This is in part due to a lack of clear guidelines as to the capability of drones and how to plan and successfully execute flights, but also due to a lack of information pertaining to specific target species and related contextual and environmental considerations. In this paper we outline a protocol for using drones to assist in the monitoring of colonies of breeding colonial waterbirds. We base the protocol on experience carrying out drone-based surveys of several colonies ranging in population from ~1000 to ~250,000 individuals. These are among the largest colonies ever surveyed via drone. We provide end-to-end guidelines, including detectability, flight planning and execution, on-ground data collection, image processing and target feature counting.

•Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking•We present a generalised semi-automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from ~20,000 to ~250,000 birds, providing estimates of bird nests•Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil etc.). Our semi-automated approach was between 3 to 8 times faster than manually counting nests from imagery at the same level of accuracy•This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution for monitoring large and complex aggregations where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open-source (e.g., Google Earth Engine and R), and generalisable to other surveys