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Improved knowledge of how habitat for water-dependent species is changing over space and time across entire river catchments is important in developing indicators for tracking changes and quantifying the effectiveness of environmentally-targeted water management actions. Such information is often difficult to obtain across large catchments, given that habitat for water-dependent species can change rapidly and typically depends on complex interactions of environmental variables. Models can help in filling these information gaps, by using incomplete data to generalise patterns at fine spatial and temporal resolution across large catchments. We developed dynamic habitat models for seven water-dependent species across the Murray-Darling Basin, the longest river system in Australia. We considered two plant species (river red gum and lignum), two waterbird species (royal spoonbill and straw-necked ibis), two fish species (Murray cod and golden perch) and a macroinvertebrate group (Decapoda). We utilised advances in basin-wide data on stream flow and inundation to derive a range of ecologically meaningful spatiotemporal habitat predictors, and used these to model occurrence and physiological condition for each species. This data-driven approach identified the environmental variables most important in predicting habitat quality for each species, and their associated response functions. The dynamic habitat models were used to generate basin-wide, fine resolution (≈90 m) predictions for each species across a time-series of several decades (1995–2020). The model predictions were summarised into indicators to identify changes in habitat quality over time and identify areas that consistently provided the highest quality habitat. These habitat models and predictions extend our understanding of the environmental drivers of habitat for each water-dependent species studied and enable a variety of assessments to quantify the ecological outcomes of past water management.

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone’s range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98–1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31–88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [−28–57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

Introduction Improving hospital oxygen systems can improve quality of care and reduce mortality for children, but we lack data on cost-effectiveness or sustainability. This study evaluated medium-term sustainability and cost-effectiveness of the Nigeria Oxygen Implementation programme.Methods Prospective follow-up of a stepped-wedge trial involving 12 secondary-level hospitals. Cross-sectional facility assessment, clinical audit (January–March 2021), summary admission data (January 2018–December 2020), programme cost data. Intervention: pulse oximetry introduction followed by solar-powered oxygen system installation with clinical and technical training and support. Primary outcomes: (i) proportion of children screened with pulse oximetry; (ii) proportion of hypoxaemic (SpO2