DNA barcoding of nematodes using the MinION

Many nematode species are parasitic and threaten the health of plants and animals, including humans, on a global scale. Advances in DNA sequencing techniques have allowed for the rapid and accurate identification of many organisms including nematodes. However, the steps taken from sample collection in the field to molecular analysis and identification can take many days and depend on access to both immovable equipment and a specialized laboratory. Here, we present a protocol to genetically identify nematodes using 18S SSU rRNA sequencing using the MinION, a portable third generation sequencer, and proof that it is possible to perform all the molecular preparations on a fully portable molecular biology lab – the Bentolab. We show that both parasitic and free-living nematode species (Anisakis simplex, Panagrellus redivivus, Turbatrix aceti, and Caenorhabditis elegans) can be identified with a 96–100% accuracy compared to Sanger sequencing, requiring only 10–15 min of sequencing. This protocol is an essential first step toward genetically identifying nematodes in the field from complex natural environments (such as feces, soil, or marine sediments). This increased accessibility could in turn improve global information of nematode presence and distribution, aiding near-real-time global biomonitoring

A preliminary assessment of using conservation drones for Sumatran orang-utan (Pongo abelii) distribution and density

To conserve biodiversity scientists monitor wildlife populations and their habitats. Current methods have constraints such as the costs of ground or aerial surveys, limited resolution of freely-available satellite images, and expensive high resolution satellite images. Recently researchers started to use unmanned aerial vehicles (aka UAVs or drones) for wildlife and habitat monitoring. Here we tested whether we could detect nests of the critically endangered Sumatran orang-utan on imagery acquired from camera mounted drone to determine distribution and density. Our results show that the distribution of nests compares well between aerial and ground based surveys and that relative density (nest/km) shows a significant correlation between these two survey types. The results also indicate that both methods can be used to detect significant differences in relative density between previously degraded reforested and enriched areas. We conclude that orang-utan nest surveys from drones are a promising survey method to determine distribution and (relative) density of this and perhaps other ape species

Measuring disturbance at a swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study

There is a growing body of research indicating that drones can disturb animals. However, it is usually unclear whether the disturbance is due to visual or auditory cues. Here, we examined the effect of drone flights on the behaviour of great dusky swifts Cypseloides senex and white-collared swifts Streptoprocne zonaris in two breeding sites where drone noise was obscured by environmental noise from waterfalls and any disturbance must be largely visual. We performed 12 experimental flights with a multirotor drone at different vertical, horizontal and diagonal distances from the colonies. From all flights, 17% caused  50 m and that recreational flights should be discouraged or conducted at larger distances (e.g. 100 m) in nesting birds areas such as waterfalls, canyons and caves

An evaluation of the factors affecting ‘poacher’ detection with drones and the efficacy of machine-learning for detection

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies

Community motivations to engage in conservation behaviour to conserve the Sumatran orangutan

Community-based conservation programs in developing countries often assume that heteronomous motivation (e.g. extrinsic incentives such as economic rewards and pressure or coercion to act) will motivate local communities to adopt conservation behaviors. However, this may not be as effective or sustainable as autonomous motivations (e.g. an intrinsic desire to act due to inherent enjoyment or self-identification with a behavior and through freedom of choice). This paper analyses the comparative effectiveness of heteronomous versus autonomous approaches to community-based conservation programs, using the example of Sumatran orangutan (Pongo abelii) conservation in Indonesia. Comparing three case study villages employing differing program designs, we found that heteronomous motivations (e.g. income from tourism) led to a change in self-reported behavior towards orangutan protection. However, they were ineffective in changing self reported behavior towards forest (i.e. orangutan habitat) protection. The most effective approach to creating self-reported behavior change throughout the community was with a combination of autonomous and heteronomous motivations. Individuals who were heteronomously motivated to protect the orangutan were found to be more likely to have changed attitudes than their self-reported behavior. These findings demonstrate that the current paradigm of motivating communities in developing countries to adopt conservation behaviors primarily through monetary incentives and rewards should also consider integrating autonomous motivational techniques which promote the intrinsic values of conservation. Such a combination will have a greater potential to achieve sustainable and cost-effective conservation outcomes. Our results highlight the importance of in-depth socio psychological analyses to assist the design and implementation of community-based conservation programs

Fresh strategies to save orangutans

The Bornean orangutan (Pongo pygmaeus) was listed as critically endangered by the International Union for Conservation of Nature this month, despite decades of conservation efforts. We urgently need fresh strategies to counteract habitat loss and hunting, and to mitigate the impacts of climate change

Counting crocodiles from the sky: Monitoring the critically endangered gharial (Gavialis gangeticus) population with an Unmanned Aerial Vehicle (UAV).

Technology is rapidly changing the methods in the field of wildlife monitoring. Unmanned aerial vehicle (UAV) is an example of a new technology that allows biologists to take to the air to monitor wildlife. Fixed Wing UAV was used to monitor critically endangered gharial population along 46 km of the Babai River in Bardia National Park. The UAV was flown at an altitude of 80 m along 12 pre-designed missions with a search effort of 2.72 hours of flight time acquired a total of 11,799 images covering an effective surface area of 8.2 km2 of river bank habitat. The images taken from the UAV could differentiate between gharial and muggers. A total count of 33 gharials and 31 muggers with observed density (per km2) of 4.64 and 4.0 for gharial and mugger respectively. Comparison of count data between one-time UAV and multiple conventional visual encounter rate surveys data showed no significant difference in the mean. Basking season and turbidity were important factors for monitoring crocodiles along the river bank habitat. Efficacy of monitoring crocodiles by UAV at the given altitude can be replicated in high priority areas with less operating cost and acquisition of high resolution data

Small room for compromise between oil palm cultivation and primate conservation in Africa

Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent’s primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge

Assessment of Chimpanzee Nests Detectability on Drone-Acquired Images

As with other species of great apes, chimpanzee numbers have declined during the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km2), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones to detect wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50x500m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability on drone-acquired images. We discuss the potential, but also the limitations of this technology for determining chimpanzee distribution and density and provide guidance for future investigation on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection on animal distribution and habitat connectivity that has important implications for apes conservation in an increasingly anthropogenically disturbed landscape

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years