Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1-5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa.Remote Sens. 2020, 12, 1185 2 of 17 semi-automated methods. The former can be extremely labour-intensive and consequently expensive, particularly for large aggregations of wildlife [26], further complicated when more than one species is counted. Semi-automated methods, including the counting of animals from photographs (e.g., camera traps) and drone imagery, are increasingly being developed around the world [27]. These methods reduce the time required to count and process drone images [28], accelerating the data entry stage and encouraging the use of drones as scientific tools for management. Such benefits allow for real-time monitoring and management decisions and could, for example, assist in the targeted delivery of environmental flows for waterbird breeding events [29].Generally, semi-automated counting methods are most effective for species where there are strong contrasts against the backgrounds, particularly when background colours and shapes are consistent [28]. They can distinguish large single species aggregations on relatively simple backgrounds [30][31][32], up to sixteen avian species (numbering in the hundreds) on simple single colour backgrounds, such as oceans [33,34], or single species aggregations of hundreds of thousands on complex backgrounds [3].Development of flexible, repeatable and efficient methods, using open source software, is important in ensuring methods are applicable across a range of datasets [35,36]. Further, there are potential cost implications of processing data, given that some processing software can be expensive (i.e., compulsory licence fees, called 'payware' in this paper) and so are often only accessible to large organisations in high-income countries [37]. Open source software, or software with optional lic...

No abstract

Almost half of Australian freshwater turtle species are formally listed as threatened, but little is known about the effects of water management and other factors on the abundance and health of freshwater turtles in arid and semi‐arid regions. This study investigated how river flows (including the controlled release of water from an upstream storage facility, or ‘environmental flow’) and water quality might affect the abundance and nutritional status of three freshwater turtle species in three dryland rivers of the Murray–Darling basin in south‐eastern Australia. No response in abundance or nutritional status of the broad‐shelled turtle, Chelodina expansa, the eastern long‐necked turtle, Chelodina longicollis, and the Macquarie turtle, Emydura macquarii, was detected in relation to river flows, possibly because of the small magnitude of the environmental flow. However, for C. expansa, the catch per unit effort (CPUE) was negatively related to increasing macrophyte cover, electrical conductivity (EC, an indicator of salinity), and turbidity. CPUE for C. longicollis was positively related to macrophyte cover and EC, and for E. macquarii it was positively related to macrophyte cover. Haematological measurements suggested that the turtles had healthy nutritional status. Body condition and blood glucose and protein were related significantly to EC, whereas haematological measurements varied significantly among species and between spring and summer. The main conclusion is that water management measures to help the conservation of these turtles should include sufficient environmental flow to produce overbank flooding, thereby creating and maintaining a wide range of habitat suitable for the differing needs of the individual species.

No abstract