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.

1Ecologists are increasingly using technology to improve the quality of data collected on wildlife, 2 particularly for assessing the environmental impacts of human activities. Remotely Piloted 3 Aircraft Systems (RPAS; commonly known as 'drones') are widely touted as a cost-effective 4 way to collect high quality wildlife population data, however, the validity of these claims is 5 unclear. Using life-sized seabird colonies containing a known number of replica birds, we show 6 that RPAS-derived data are, on average, between 43% and 96% more accurate than data from 7 the traditional ground-based collection method. We also demonstrate that counts from this 8 remotely sensed imagery can be semi-automated with a high degree of accuracy. The 9 increased accuracy and precision of RPAS-derived wildlife monitoring data provides greater 10 statistical power to detect fine-scale population fluctuations allowing for more informed and 11 proactive ecological management. 12

The use of unmanned aerial vehicles (UAVs), colloquially referred to as 'drones', for biological field research is increasing [1-3]. Small, civilian UAVs are providing a viable, economical tool for ecology researchers and environmental managers. UAVs are particularly useful for wildlife observation and monitoring as they can produce systematic data of high spatial and temporal resolution [4]. However, this new technology could also have undesirable and unforeseen impacts on wildlife, the risks of which we currently have little understanding [5-7]. There is a need for a code of best practice in the use of UAVs to mitigate or alleviate these risks, which we begin to develop here.

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Invasive species threaten endangered species worldwide and substantial effort is focused on their control. Eradication projects require critical resource allocation decisions, as they affect both the likelihood of success and the overall cost. However, these complex decisions must often be made within data-poor environments. Here we develop a mathematical framework to assist in resource allocation for invasive species control projects and we apply it to the proposed eradication of the tropical fire ant (Solenopsis geminata) from the islands of Ashmore Reef in the Timor Sea. Our framework contains two models: a population model and a detection model. Our stochastic population model is used to predict ant abundance through time and allows us to estimate the probability of eradication. Using abundance predictions from the population model, we use the detection model to predict the probability of ant detection through time. These models inform key decisions throughout the project, which include deciding how many baiting events should take place, deciding whether to invest in detector dogs and setting surveillance effort to confirm eradication following control. We find that using a combination of insect growth regulator and toxins are required to achieve a high probability of eradication over 2 years, and we find that using two detector dogs may be more cost-effective than the use of lure deployment, provided that they are used across the life of the project. Our analysis lays a foundation for making decisions about control and detection throughout the project and provides specific advice about resource allocation.

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