Unmanned aerial vehicles (UAVs) represent a new frontier in environmental research. Their use has the potential to revolutionise the field if they prove capable of improving data quality or the ease with which data are collected beyond traditional methods. We apply UAV technology to wildlife monitoring in tropical and polar environments and demonstrate that UAV-derived counts of colony nesting birds are an order of magnitude more precise than traditional ground counts. The increased count precision afforded by UAVs, along with their ability to survey hard-to-reach populations and places, will likely drive many wildlife monitoring projects that rely on population counts to transition from traditional methods to UAV technology. Careful consideration will be required to ensure the coherence of historic data sets with new UAV-derived data and we propose a method for determining the number of duplicated (concurrent UAV and ground counts) sampling points needed to achieve data compatibility.

Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA; commonly known as “drones,” unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost‐effective way to collect high‐quality wildlife population data, the validity of these claims is unclear. Using life‐sized, replica seabird colonies containing a known number of fake birds, we assessed the accuracy of RPA‐facilitated wildlife population monitoring compared to the traditional ground‐based counting method. The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. We show that RPA‐derived data are, on average, between 43% and 96% more accurate than the traditional ground‐based data collection method. We also demonstrate that counts from this remotely sensed imagery can be semi‐automated with a high degree of accuracy. The increased accuracy and increased precision of RPA‐derived wildlife monitoring data provides greater statistical power to detect fine‐scale population fluctuations allowing for more informed and proactive ecological management.

Quantifying habitat quality is dependent on measuring a site's relative contribution to population growth rate. This is challenging for studies of waterbirds, whose high mobility can decouple demographic rates from local habitat conditions and make sustained monitoring of individuals near‐impossible. To overcome these challenges, biologists have used many direct and indirect proxies of waterbird habitat quality. However, consensus on what methods are most appropriate for a given scenario is lacking. We undertook a structured literature review of the methods used to quantify waterbird habitat quality, and provide a synthesis of the context‐dependent strengths and limitations of those methods. Our search of the Web of Science and Scopus databases returned a sample of 666 studies, upon which our review was based. The reviewed studies assessed habitat quality by either measuring habitat attributes (e.g., food abundance, water quality, vegetation structure), or measuring attributes of the waterbirds themselves (e.g., demographic parameters, body condition, behavior, distribution). Measuring habitat attributes, although they are only indirectly related to demographic rates, has the advantage of being unaffected by waterbird behavioral stochasticity. Conversely, waterbird‐derived measures (e.g., body condition, peck rates) may be more directly related to demographic rates than habitat variables, but may be subject to greater stochastic variation (e.g., behavioral change due to presence of conspecifics). Therefore, caution is needed to ensure that the measured variable does influence waterbird demographic rates. This assumption was usually based on ecological theory rather than empirical evidence. Our review highlighted that there is no single best, universally applicable method to quantify waterbird habitat quality. Individual project specifics (e.g., time frame, spatial scale, funding) will influence the choice of variables measured. Where possible, practitioners should measure variables most directly related to demographic rates. Generally, measuring multiple variables yields a better chance of accurately capturing the relationship between habitat characteristics and demographic rates.

The distribution of wildlife informs conservation planning. In marine systems, spatial information for seabirds is acquired primarily using vessel-based at-sea survey, aerial survey or animal-borne tracking devices. Many factors contribute to geographic biases in marine spatial research and we use a structured database search to review global patterns resulting from these biases. Although survey effort has been substantial, many large marine sectors were poorly represented. Poor representation often coincided spatially with hotspots of seabird species richness or areas experiencing high cumulative human impact, particularly in southern hemisphere and equatorial regions. Therefore, future seabird research priorities should reflect high species-richness areas including the south-west Pacific, particularly waters surrounding New Zealand. Furthermore, under-represented areas of Asia, and the Atlantic coasts of South America and Africa require greater research effort because of high cumulative human impacts in these areas. National gross domestic product (GDP) was positively related to the number of papers a country produced (determined by the first author’s address). This is indicative of constraints imposed by inherent costs of marine research. We recommend international collaboration between scientists from high-GDP nations with those in developing countries to address this. Further uptake of platforms of opportunity may also reduce this imbalance.

It is imperative to manage wetlands appropriately in the non‐breeding range of migratory shorebirds because their habitat quality impacts survival and reproductive performance. However, it is challenging to measure and relate these parameters to the habitat quality of individual sites because they are influenced by a composite contribution to fitness across all sites used in the annual cycle. Identifying important habitat quality features for shorebirds, and assessing whether management actions aimed at improving habitat quality are effective, poses a substantial challenge. Nonetheless, within a site, shorebird subsite selection and foraging behaviour could reveal variation in habitat quality proxies and suitability. We developed a field study to investigate habitat selection and habitat quality proxies for shorebirds in the Coorong, an internationally important non‐breeding site in Australia. We examined the abundance of shorebirds among multiple subsites in the wetland and their foraging behaviour in relation to multiple variables (potential habitat area, salinity, prey density, wind, temperature), the first three of which can be influenced by management. Larger potential habitat area, defined as the combined area of bare mud/sand and shallow water (<20 cm depth), was associated with higher shorebird abundance across subsites with the same shore length, suggesting this is the most important site feature for habitat selection. Shorebird step rate, which reflects the effort associated with catching prey, was higher at subsites with lower prey density. This suggests that step rate can be used as an indicator of prey availability. Potential habitat area is strongly positively correlated with shorebird abundance. At sites where water levels can be managed, flows should be regulated so that water levels are not too high (limiting habitat area) or too low (drying out mudflats). However, it is critical that large potential habitat areas that attract shorebirds are productive so that they do not become ecological traps. Management should aim to maintain healthy prey abundance on areas attractive for shorebirds, while also minimising predation and disturbance of shorebirds. Monitoring prey abundance directly can be challenging, but our study shows that shorebird step rate provides a low‐cost indicator of relative macroinvertebrate prey abundance.