Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.

Background: Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals. Methods: We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. Review findings: We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review. Conclusions: Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images.

Surveying animal populations using drones (unoccupied aircraft systems [UAS]) provides numerous advantages; however, few best practices exist to survey animal communities with drones. Among myriad factors that can affect human identification and counts of animals from drone images, we focused on three factors typically controlled in the study design or by the drone pilot: flight altitude, camera angle, and time of day. Identifying interactions and patterns among these three variables represents an important first step to determining best survey practices. We used a drone to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four ground sampling distance (GSD) values (0.35, 0.70, 1.06, and 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, and 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals from drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday than during the morning and afternoon, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy more than the 90° camera, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations for improving human accuracy in identifying and counting animals from drone images used in monitoring animal populations and communities. These results should be incorporated into drone survey design in addition to considering funding, logistical, and animal behavior constraints.