Antarctic marine ecosystems undergo enormous changes, presumably due to climate change and fishery. Unmanned aerial vehicles (UAVs) have an unprecedented potential for measuring these changes by mapping indicator species such as penguins even in remote areas. We used a battery-powered fixed-wing UAV to survey colonies along a 30-km stretch of the remote coast of southwest King George Island and northwest Nelson Island (South Shetland Islands, Antarctica) during the austral summer 2016/17. With multiple flights, we covered a total distance of 317 km. We determined the exact position of 14 chinstrap penguin colonies, including two small unknown colonies, with a total abundance of 35,604 adults. To model the number of occupied nests based on the number of adults counted in the UAV imagery we used data derived from terrestrial time-lapse imagery. The comparison with previous studies revealed a decline in the total abundance of occupied nests. However, we also found four chinstrap penguin colonies that have grown since the 1980s against the general trend on the South Shetland Islands. The results proved the suitability of the use of small and lightweight fixed-wing UAVs with electric engines for mapping penguin colonies in remote areas in the Antarctic.

Due to the remote location of colonies of Antarctic shags (Phalacrocorax(atriceps)bransfieldensis) in Antarctica, there is only sparse data on the abundance of this species. An unmanned aerial vehicle (UAV) survey for known and unknown Antarctic shag colonies along the coasts of Nelson Island and western King George Island, Antarctica, was conducted in December 2016. Four colonies, one of them previously unknown, were detected. For the first time since the 1980s, the total population size of the colonies in that area was determined. A comparison with previous estimates revealed evidence of a population increase by a factor of 2.86. To support future survey campaigns, several characteristic features of Antarctic shag colonies, nests and individuals in aerial imagery were identified. This makes possible more reliable detection and determination of population size in Antarctic shag colonies. These characteristic features were compared with those of chinstrap penguin colonies (Pygoscelis antarcticus) because these species often overlap spatially and are difficult to distinguish. In addition, the optimal weather conditions and flight parameters for an aerial survey were specified.

In recent years Unmanned Aerial Vehicles (UAVs) became a fast-developing technology with manifold fields of application. In the field of wildlife biology, it offers the opportunity to quantify populations, to map the spatial distribution of species and to observe the behavior of animals with no or low disturbance. Moreover, UAV based mapping allows to obtain data from sites which are hardly or not accessible and to cover much larger areas than by traditional ground based methods. The advantages of UAV based mapping are of particular relevance under the harsh conditions of Antarctic fieldwork. Whether certain species qualify for UAV based monitoring depends on their detectability from the distance and the distinctiveness of their characteristics in comparison to other species, which has not been studied for Antarctic species in detail so far. This study aims to evaluate how and under which conditions, particularly flight height, Antarctic flying seabird and seal species are detectable in aerial imagery. A trial was conducted comparing the detection rate of different observers for several Antarctic species in aerial images of different ground sample distances. Descriptions of individual appearance as well as body size dimensions are delivered for all species. For most of the investigated species, monitoring proves to be possible from practical flight heights, while others are still very hard to detect even in low altitudes. A concluding table is given aiming to provide a guide for future surveys on which flight altitudes to chose and how to identify focal study objects.

Using 51 orthomosaics of 11 breeding locations of the Antarctic shag, we propose a method for automating counting of shag nests. This is achieved by training an object detection model based on the YOLO architecture and identifying nests on sections of the orthomosaic, which are later combined with predictions for the entire orthomosaic. Our results show that the current use of Remotely Piloted Aircraft Systems (RPAS) to collect images of areas with shag colonies, combined with machine learning algorithms, can provide reliable and fast estimates of shag nest counts (F1 score > 0.95). By using data from only two shag colonies for training, we show that models can be obtained that generalise well to images of both spatially and temporally distinct colonies. The proposed practical application opens the possibility of using aerial imagery to perform large-scale surveys of Antarctic islands in search of undiscovered shag colonies. We discuss the conditions for optimal performance of the model as well as its limitations. The code, data and trained model allowing for full reproducibility of the results are available at https://github.com/Appsilon/Antarctic-nests .

Emperor penguins (Aptenodytes forsteri) breed on the remote coastlines of Antarctica under the extreme conditions of the polar winter. Consequently, the majority of their colonies have never been visited by humans. Anthropogenic climate change and the associated profound shifts in the Antarctic ecosystem now threaten the survival of the species. Due to the extreme remoteness of these colonies, most current population estimates rely on satellite imagery or aircraft surveys, both of which are subject to considerable uncertainties.,In this study, we investigate for the first time the suitability of aerial drones for determining the population size of emperor penguin colonies. To this end, the Atka Bay colony was surveyed multiple times with an aerial drone during a single breeding season. In addition, we evaluated data from 13 aircraft surveys across 5 emperor penguin colonies in Queen Maud Land collected between 2022 and 2025. Our analysis focused on quantifying observer error and modelling breeding pair numbers based on counts of individuals.,As a result, we provide what are likely the most accurate breeding pair estimates for these colonies to date. We also quantified the distinct influence of ground resolution, lighting conditions, and guano staining on observer error. Our findings demonstrate that drones are highly suitable for surveying local emperor penguin colonies, while also highlighting that deriving breeding pair estimates from the number of individuals at the end of the season remains subject to substantial uncertainty.