Rapid literature mapping on the recent use of machine learning for wildlife imagery

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases

The Application of a New Mass Spectrometry Technique Using Non-Invasive Biological samples for Conservation and Ecology Studies

Effective conservation strategies are required to increase biodiversity. More information about a species increases the chances of choosing policies that enhance survival. Many historical population monitoring methods, such as live trapping, were invasive or difficult to implement for particular species or locations. This has led to the development of molecular analysis of non-invasive samples, including faeces. Faecal samples are easy to collect and store; they are a potentially rich source of information and do not require direct observation of the animal. Faecal samples can be used to obtain genetic information, but techniques are labour-intensive and time-consuming, and the abundance of hormones in faecal samples degrades with time. The potential of a new ambient mass spectrometry technique to analyse faecal samples was investigated in this study. Rapid Evaporative Ionisation Mass spectrometry (REIMS) was developed for medicine to distinguish between cancerous and healthy tissue. It was used in food security to determine if the origin of a food source is as advertised. We have shown that REIMS can discriminate different species of rodents. This study aimed to determine the scope of REIMS to differentiate faecal samples of laboratory animals by analysing the sex, maturity and strain of different lab mice. The discrimination ability of REIMS was also explored in captive zoo animals to determine whether REIMS could be used to detect pregnancy. The practicality of REIMS in field studies was tested, and the ability to use REIMS as a method for population monitoring was determined by establishing the species distribution of rodents in multiple field sites. The power of REIMS to distinguish between more subtle differences; sex, maturity, strain and pregnancy; was limited compared to species. The composition of faecal samples did change with storage time, but REIMS could still distinguish between species of samples that had been in the freezer for over two years. REIMS established the species distribution of three rodent species across four field sites. Therefore, REIMS can be used as an additional non-invasive method to aid conservation and ecology studies

Towards Automatic Detection of Animals in Camera-Trap Images

In recent years the world's biodiversity is declining on an unprecedented scale. Many species are endangered and remaining populations need to be protected. To overcome this agitating issue, biologist started to use remote camera devices for wildlife monitoring and estimation of remaining population sizes. Unfortunately, the huge amount of data makes the necessary manual analysis extremely tedious and highly cost intensive. In this paper we re-train and apply two state-of-the-art deep-learning based object detectors to localize and classify Serengeti animals in camera-trap images. Furthermore, we thoroughly evaluate both algorithms on a self-established dataset and show that the combination of the results of both detectors can enhance overall mean average precision. In contrast to previous work our approach is not only capable of classifying the main species in images but can also detect them and therefore count the number of individuals which is in fact an important information for biologists, ecologists, and wildlife epidemiologists