Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts

Drone Observation for the Quantitative Study of Complex Multilevel Societies

Unmanned aerial vehicles (drones) have recently been used in various behavioral ecology studies. However, their application has been limited to single groups, and most studies have not implemented individual identification. A multilevel society refers to a social structure in which small stable "core units" gather and make a larger, multiple-unit group. Here, we introduce recent applications of drone technology and individual identification to complex social structures involving multiple groups, such as multilevel societies. Drones made it possible to obtain the identification, accurate positioning, or movement of more than a hundred individuals in a multilevel social group. In addition, in multilevel social groups, drones facilitate the observation of heterogeneous spatial positioning patterns and mechanisms of behavioral propagation, which are different from those in a single-level group. Such findings may contribute to the quantitative definition and assessment of multilevel societies and enhance our understanding of mechanisms of multiple group aggregation. The application of drones to various species may resolve various questions related to multilevel societies.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Counting animals in aerial images with a density map estimation model

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual postprocessing has been used extensively; however, volumes of such data are increasing, necessitating some level of automation, either for complete counting, or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster-RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations and demonstrably improve monitoring efforts from aerial imagery.

Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape

The Vulnerable marsh deer Blastocerus dichotomus, the largest native cervid in South America, is declining throughout its range as a result of the conversion of wetlands and overhunting. Estimated densities in open wetlands of several types are 0.1–6.8 individuals per km2. We undertook the first unmanned aerial vehicle (UAV) survey of the marsh deer to estimate the density of this species in a 113.6 km2 area under forestry management in the lower delta of the Paraná River, Argentina. During 6–8 August 2019, at a time of year when canopy cover is minimal, we surveyed marsh deer using Phantom 4 Pro UAVs along 94 transects totalling 127.8 km and 8.6 km2 (8.1% of the study area). The 5,506 photographs obtained were manually checked by us and by a group of 39 trained volunteers, following a standardized protocol. We detected a total of 58 marsh deer, giving an estimated density of 6.90 individuals per km2 (95% CI 5.26–8.54), which extrapolates to 559–908 individuals in our 113.6 km2 study area. As it has generally been assumed that marsh deer prefer open habitats, this relatively high estimate of density within a forestry plantation matrix is unexpected. We discuss the advantages of using UAVs to survey marsh deer and other related ungulates.

Integrating satellite and unmanned aircraft system (UAS) imagery to model livestock population dynamics in the Longbao Wetland National Nature Reserve, China

The collection of field-based animal data is laborious, risky and costly in some areas, such as various nature reserves. Although multiple studies have used satellite imagery, aerial imagery, and field data individually for some animal species surveys, several technical issues still need to be addressed before full standardization of remote sensing methods for modeling animal population dynamics over large areas. This study is the first to model the population dynamics of livestock in the Longbao Wetland National Nature Reserve, China by utilizing yak estimations from Worldview-2 satellite imagery (0.5 m) collected in 2010 and yaks counted in a ground-based survey conducted in 2011 in combination with the animal population structure precisely extracted from UAS imagery captured in 2016. As a consequence, 5501, 5357, and 5510 yaks were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. In total, 1092, 1062 and 1092 sheep were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. The uncertainty of the presented method is also discussed. Primary experiments show that both the satellite imagery and UAS imagery are promising for use in yak censuses, but no sheep were observed in the satellite imagery because of the low resolution. Compared to the ground-based survey conducted in 2011, the UAS image estimate and satellite imagery count deviated in yak quantity by 2.69% and 2.86%, respectively. UASs are a reliable and low-budget alternative to animal surveys. No discernable changes in animal behaviors and animal distributions were observed as the UAS passed at a height of 700 m, and the accuracy of UAS imagery counts were not significantly affected by the short-distance animal movement and image mosaicking errors. The experimental results illustrate the advantages of the combination of satellite and UAS imagery in modeling animal population dynamics.

Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

This article reviews studies regarding wild animal surveys based on multiple platforms, including satellites, manned aircraft, and unmanned aircraft systems (UASs), and focuses on the data used, animal detection methods, and their accuracies. We also discuss the advantages and limitations of each type of remote sensing data and highlight some new research opportunities and challenges. Submeter very-high-resolution (VHR) spaceborne imagery has potential in modeling the population dynamics of large (>0.6 m) wild animals at large spatial and temporal scales, but has difficulty discerning small (<0.6 m) animals at the species level, although high-resolution commercial satellites, such as WorldView-3 and -4, have been able to collect images with a ground resolution of up to 0.31 m in panchromatic mode. This situation will not change unless the satellite image resolution is greatly improved in the future. Manned aerial surveys have long been employed to capture the centimeter-scale images required for animal censuses over large areas. However, such aerial surveys are costly to implement in small areas and can cause significant disturbances to wild animals because of their noise. In contrast, UAS surveys are seen as a safe, convenient and less expensive alternative to ground-based and conventional manned aerial surveys, but most UASs can cover only small areas. The proposed use of UAS imagery in combination with VHR satellite imagery would produce critical population data for large wild animal species and colonies over large areas. The development of software systems for automatically producing image mosaics and recognizing wild animals will further improve survey efficiency.

Inferring the rules of social interaction in migrating caribou

Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa.

This article is part of the theme issue ‘Collective movement ecology’.

Simplified procedure for efficient and unbiased population size estimation

Population size estimation is relevant to social and ecological sciences. Exhaustive manual counting, the density method and automated computer vision are some of the estimation methods that are currently used. Some of these methods may work in concrete cases but they do not provide a fast, efficient and unbiased estimation in general. Recently, the CountEm method, based on systematic sampling with a grid of quadrats, was proposed. It offers an unbiased estimation that can be applied to any population. However, choosing suitable grid parameters is sometimes cumbersome. Here we define a more intuitive grid parametrization, using initial number of quadrats and sampling fraction. A crowd counting dataset with 51 images and their corresponding, manually annotated position point patterns, are used to analyze the variation of the coefficient of error with respect to different parameter choices. Our Monte Carlo resampling results show that the error depends on the sample size and the number of nonempty quadrats, but not on the size of the target population. A procedure to choose suitable parameter values is described, and the expected coefficients of error are given. Counting about 100 particles in 30 nonempty quadrats usually yields coefficients of error below 10%.

Challenges and solutions for studying collective animal behaviour in the wild

Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.This article is part of the theme issue 'Collective movement ecology'.

A computer vision for animal ecology

A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.