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Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1-20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward's clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.

[This corrects the article DOI: 10.1371/journal.pone.0172065.].[This corrects the article DOI: 10.1371/journal.pone.0172065.].

Assessing the numbers and distribution of at-risk megafauna such as the black rhino ( Diceros bicornis ) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals ( e.g ., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework has a lower hardware cost and can function with a reduced internet bandwidth requirement for local network communication. It proposes the use of a Jetson Xavier NX, onboard a Parrot Anafi drone, connected to the internet throughout the flight to deliver a lightweight web-based notification system upon detection of the target species. The GPS location with the detected target species images is sent using MQ Telemetry Transport (MQTT), a lightweight messaging protocol using a publisher/subscriber architecture for IoT devices. It provides reliable message delivery when internet connection is sporadic. We used a YOLOv5l6 object detection architecture trained to identify a bounding box for one of five objects of interest in a frame of video. At an intersection over union (IoU) threshold of 0.5, our model achieved an average precision (AP) of 0.81 for black rhino (our primary target) and 0.83 for giraffe ( Giraffa giraffa) . The model was less successful at identifying the other smaller objects which were not our primary targets: 0.34, 0.25, and 0.42 for ostrich ( Struthio camelus australis ), springbok ( Antidorcas marsupialis ) and human respectively. We used several techniques to optimize performance and overcome the inherent challenge of small objects (animals) in the data. Although our primary focus for the development of the model was rhino, we included other species classes to emulate field conditions where many animal species are encountered, and thus reduce the false positive occurrence rate for rhino detections. To constrain model overfitting, we trained the model on a dataset with varied terrain, angle and lighting conditions and used data augmentation techniques ( i.e ., GANs). We used image tiling and a relatively larger ( i.e ., higher resolution) image input size to compensate for the difficulty faced in detecting small objects when using YOLO. In this study, we demonstrated the potential of a drone-based AI pipeline model to automate the detection of free-ranging megafauna detection in a remote setting and create alerts to a wildlife manager in a relatively poorly connected field environment.

Routinely censusing rhinoceros’ populations is central to their conservation and protection from illegal killing. In Namibia, both white ( Ceratotherium simum ) and black ( Diceros bicornis ) rhinoceros occur on private land, in the latter case under a custodianship program of the Namibian Ministry of Environment and Tourism (MET). Black rhinoceros custodian landowners are responsible for the protection of the rhinoceroses on their land and are required to report regularly to the MET. Monitoring imposes a financial burden on custodians yet many of the techniques used involve expensive monitoring techniques that include the need for aerial support and/or animal instrumentation. During May and June 2018, WildTrack undertook a pilot study to census black and white rhinoceros on three private custodianship properties in Namibia. We tested three footprint identification methods for obtaining estimates of rhinoceros populations in an effort to provide less costly alternative monitoring options to rhinoceros custodians. The first was a full monitoring protocol with two components: (a) tracking each individual animal and matching them to their footprints, (b) identifying those individuals from the heel lines on the prints. The second method used simple visual heel line identification ex-situ, and the third method used just an objective footprint identification technique. These methods offer different options of fieldwork labour and cost and were designed to offer monitoring options to custodians that provided information about rhinoceros movement and location, with minimal disturbance to the rhinoceros, and best matched their human and economic resources. In this study, we describe the three methods and report the results of the pilot study to compare and evaluate their utility for rhinoceros monitoring. The first method successfully matched each trail photographed to a known rhinoceros at each site. When the other two methods disagreed with the first, they did so by failing to match single trails to a known rhinoceros, thereby creating fictitious identities consisting of a single trail. This failure occurred twice in one application, but otherwise at most once. We expect this failure can be eliminated through more stringent criteria for collecting photographs of footprints. We also briefly compare the use of footprint monitoring with other commonly used monitoring techniques. On this basis, landowners hosting rhinoceros can evaluate which method best suits their needs and resources.

Monitoring free-ranging animals in their natural habitat is a keystone of ecosystem conservation and increasingly important in the context of current rates of loss of biological diversity. Data collected from individuals of endangered species inform conservation policies. Conservation professionals assume that these data are reliable—that the animals from whom data are collected are representative of the species in their physiology, ecology, and behavior and of the populations from which they are drawn. In the last few decades, there has been an enthusiastic adoption of invasive techniques for gathering ecological and conservation data. Although these can provide impressive quantities of data, and apparent insights into animal ranges and distributions, there is increasing evidence that these techniques can result in animal welfare problems, through the wide-ranging physiological effects of acute and chronic stress and through direct or indirect injuries or compromised movement. Much less commonly, however, do conservation scientists consider the issue of how these effects may alter the behavior of individuals to the extent that the data they collect could be unreliable. The emerging literature on the immediate and longer-term effects of capture and handling indicate it can no longer be assumed that a wild animal's survival of the process implies the safety of the procedure, that the procedure is ethical, or the scientific validity of the resulting data. I argue that conservation professionals should routinely assess study populations for negative effects of their monitoring techniques and adopt noninvasive approaches for best outcomes not only for the animals, but also for conservation science. Monitorear animales de libre distribución en su ambiente natural es clave en la conservación de ecosistemas y de creciente importancia en el contexto de las tasas actuales de pérdida de la diversidad biológica. Los datos colectados de individuos de especies en peligro informan a las políticas de conservación. Los conservacionistas suponen que estos datos son confiables, es decir que los animales de los cuales los datos son colectados son representativos de la fisiología, ecología y el comportamiento de la especie y de todas las poblaciones de donde son tomados. En las últimas décadas ha habido una adopción entusiasta de técnicas invasivas para la colecta de datos ecológicos y de conservación. Aunque éstas pueden proporcionar cantidades impresionantes de datos y supuesta penetración hacia los rangos y distribución de los animales hay creciente evidencia de que estas técnicas pueden resultar en problemas de bienestar animal a través de los amplios efectos fisiológicos de estrés crónico y agudo y por medio de movimiento dificultado o heridas directas o indirectas. Sin embargo, los conservacionistas pocas veces consideran el problema de cómo estos efectos pueden alterar el comportamiento de los individuos hasta el punto en el que los datos que recopilen sean desconfiables. La literatura reciente sobre los efectos inmediatos y a largo plazo de la captura y el manejo indican que ya no se puede suponer que la supervivencia de un animal silvestre al proceso implica la seguridad del procedimiento, que el procedimiento sea ético o la validez científica de los datos resultantes. Yo explico que los conservacionistas deberían evaluar rutinariamente estudios poblaciones para saber si hay efectos negativos de las técnicas de monitoreo y adoptar aproximaciones no-invasivas para el mejor resultado no solamente para los animales sino también para la ciencia de la conservación.

As a landscape architect and a major seed disperser, the lowland tapir ( ) is an important indicator of the ecological health of certain habitats. Therefore, reliable data regarding tapir populations are fundamental in understanding ecosystem dynamics, including those associated with the Atlantic Forest in Brazil. Currently, many population monitoring studies use invasive tagging with radio or satellite/Global Positioning System (GPS) collars. These techniques can be costly and unreliable, and the immobilization required carries physiological risks that are undesirable particularly for threatened and elusive species such as the lowland tapir. We collected data from one of the last regions with a viable population of lowland tapir in the south-eastern Atlantic Forest, Brazil, using a new non-invasive method for identifying species, the footprint identification technique (FIT). We identified the minimum number of tapirs in the study area and, in addition, we observed that they have overlapping ranges. Four hundred and forty footprints from 46 trails collected from six locations in the study area in a landscape known to contain tapir were analyzed, and 29 individuals were identified from these footprints. We demonstrate a practical application of FIT for lowland tapir censusing. Our study shows that FIT is an effective method for the identification of individuals of a threatened species, even when they lack visible natural markings on their bodies. FIT offers several benefits over other methods, especially for tapir management. As a non-invasive method, it can be used to census or monitor species, giving rapid feedback to managers of protected areas.

For those vertebrate species that create sufficiently complex footprints, identifying individuals from their footprints promises to be a noninvasive technique of great potential for wildlife studies and conservation, but with statistical challenges. Various approaches to employing footprints for identification appear in the literature, but doubt often remains as to the information contained in the footprints and therefore of the reliability of the procedures. For footprints represented by landmarks, we propose using pre-assigned measures of shape and size of configurations of landmarks to quantify the variation in footprints amongst individuals relative to the variation in each individual's footprints. Our method provides a relatively simple means of assessing when footprints (represented by landmarks) from individuals of a population will be useful for identifying individuals, independent of any particular identification algorithm, and is also a tool for exploring footprint landmark data to aid development of discrimination routines. We illustrate the method using footprints collected from a population of white rhinoceros (Ceratotherium simum) at Otjiwa Game Ranch, Namibia, during late 1999.

The Amur tiger (Panthera tigris altaica) population in China, once widespread, is now reduced to an estimated 20 individuals widely dispersed over a large area. The Chinese government is making concerted efforts to restore this population from the contiguous Russian population. However, they face a challenge in finding an effective monitoring technique. We report on the development of a robust, non-invasive and cost-effective technique to identify the sex of Amur tigers from snow footprints. Between December 2011 and December 2012, we collected 523 digital images of left-hind footprints from 40 known captive Amur tigers (19 F, 21 M), of age range 3–13 years (F mean age = 8.07 ± 0.18, M mean age = 8.36 ± 0.19; F = 1.18, P>0.05). Images were captured with compact digital cameras according to a standardized photographic protocol (Alibhai et al. 2008). Using JMP software from the SAS Institute, 128 measurements were taken from each footprint according to the protocol developed by Alibhai et al. (2008), and were subjected to a stepwise selection. With just 10 variables, and testing with both Jackknifing and 50% holdout methods, the resulting algorithm for sex determination gave 98% accuracy for individual footprints. The algorithm derived from captive tiger footprints of known sex was then used to identify the sex of 83 footprints from 8 trails collected from unknown free-ranging Amur tigers in the winter from the end of 2011 to the beginning of 2012. The algorithm predicted 5 trails from females and 3 from males. This technique is a potentially valuable tool for monitoring the recovery of Amur tiger populations at the landscape scale in northeastern China.