Methods for Monitoring Large Terrestrial Animals in the Wild

The applicability of human mobility scaling laws on animals-A Herring Gull case study

With the development of sensors, recording and availability of high-resolution movement data from animals and humans, two disciplines have rapidly developed: human mobility and movement ecology. Addressing methodological gaps between these two mobility fields could improve the understanding of movement processes and has been defined as the Integrated Science of Movement. We apply well-known human mobility metrics and data processing methods to Global Positioning System (GPS) tracking data of European Herring Gulls (Larus argentatus) to test the usefulness of these methods for explaining animal mobility behavior. We use stop detection, spatial aggregation, and for the first time on animal movement data, two approaches to temporal aggregation (Next Time-Bin and Next Place). We also calculate from this data a set of movement statistics (visitation frequency, distinct locations over time, and radius of gyration). Furthermore, we analyze and compare the gull and human data from the perspective of scaling laws commonly used for human mobility. The results confirm those of previous studies and indicate differences in movement parameters between the breeding season and other parts of the year. This paper also shows that methods used in human mobility analysis have the potential to improve our understanding of animal behavior.

Digital inventory of agricultural land plots in the Kemerovo Region

Cadastral and geodetic land works are expensive, which makes aerial photography extremely valuable for land traceability and inventory. The present research objective was to develop a new digital survey technology for registration of agricultural lands. We assessed the accuracy of the new method and evaluated its decision support options. The study featured the case of the Kemerovo Region (Kuzbass), Russia. The aerial survey took place in 2021 and involved 17 municipalities of the Kemerovo Region. The software and hardware complex included an unmanned aerial vehicle (UAV) and a module for aerial photography. Photogrammetric, cartometric, and satellite methods were used to define the coordinates of feature points. We developed new software (Sovhoz.avi) to perform the land inventory. The photogrammetric and cartographic methods proved efficient in determining the feature points and boundaries of land plots. They also appeared accurate enough for land inventory and decision support. The study updated the available land inventory data. About 30% of all land plots were recorded incorrectly; some plots marked as agricultural appeared to belong to the local forest reserves or urban territories. Incorrect data (1.64%) were excluded from the official inventory. The survey covered a total area of 41 000 ha and revealed 1700 illegally used land plots. The updated inventory of unused lands included 3825 new plots (163 400 ha), which can attract prospective investors. The results can be used by the local authorities to make land management decisions and identify illegal land use.

The effects of population management on wild ungulates: A systematic map of evidence for UK species

Introduction

Over recent decades, the abundance and geographic ranges of wild ungulate species have expanded in many parts of Europe, including the UK. Populations are managed to mitigate their ecological impacts using interventions, such as shooting, fencing and administering contraception. Predicting how target species will respond to interventions is critical for developing sustainable, effective and efficient management strategies. However, the quantity and quality of evidence of the effects of interventions on ungulate species is unclear. To address this, we systematically mapped research on the effects of population management on wild ungulate species resident in the UK.

Methods

We searched four bibliographic databases, Google Scholar and nine organisational websites using search terms tested with a library of 30 relevant articles. Worldwide published peer-reviewed articles were considered, supplemented by ‘grey’ literature from UK-based sources. Three reviewers identified and screened articles for eligibility at title, abstract and full-text levels, based on predefined criteria. Data and metadata were extracted and summarised in a narrative synthesis supported by structured graphical matrices.

Results

A total of 123 articles were included in the systematic map. Lethal interventions were better represented (85%, n = 105) than non-lethal interventions (25%, n = 25). Outcomes related to demography and behaviour were reported in 95% of articles (n = 117), whereas effects on health, physiology and morphology were studied in only 11% of articles (n = 14). Well-studied species included wild pigs (n = 58), red deer (n = 28) and roe deer (n = 23).

Conclusions

Evidence for the effects of population management on wild ungulate species is growing but currently limited and unevenly distributed across intervention types, outcomes and species. Priorities for primary research include: species responses to non-lethal interventions, the side-effects of shooting and studies on sika deer and Chinese muntjac. Shooting is the only intervention for which sufficient evidence exists for systematic review or meta-analysis.

Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Alces alces) in Salair Mountains

Simple Summary

Forest animals can be used as a sensitive indicator of the real state of biodiversity. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures of −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate information in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian, Canadian and Alaskan winter forests.

Abstract

There are two main reasons for monitoring the population of forest animals. First, regular surveys reveal the real state of biodiversity. Second, they guarantee a prompt response to any negative environmental factor that affects the animal population and make it possible to eliminate the threat before any permanent damage is done. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate statistics in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The authors developed an effective methodology for processing the data obtained from drone-mounted thermal infrared cameras. The research provided reliable results concerning the changes in the elk population on the territory in question. The use of drone planes proved an effective means of ungulate animal surveying in snow-covered winter forests. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian and Canadian winter forests.

Automated Detection of Koalas with Deep Learning Ensembles

Effective management of threatened and invasive species requires regular and reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they are relatively inexpensive. They enable larger areas to be surveyed than traditional methods for many species, particularly cryptic species such as koalas, with less disturbance. The development of robust and accurate methods for species detection is required to effectively use the large volumes of data generated by this survey method. The enhanced predictive and computational power of deep learning ensembles represents a considerable opportunity to the ecological community. In this study, we investigate the potential of deep learning ensembles built from multiple convolutional neural networks (CNNs) to detect koalas from low-altitude, drone-derived thermal data. The approach uses ensembles of detectors built from combinations of YOLOv5 and models from Detectron2. The ensembles achieved a strong balance between probability of detection and precision when tested on ground-truth data from radio-collared koalas. Our results also showed that greater diversity in ensemble composition can enhance overall performance. We found the main impediment to higher precision was false positives but expect these will continue to reduce as tools for geolocating detections are improved. The ability to construct ensembles of different sizes will allow for improved alignment between the algorithms used and the characteristics of different ecological problems. Ensembles are efficient and accurate and can be scaled to suit different settings, platforms and hardware availability, making them capable of adaption for novel applications.

Is Ecotourism an Opportunity for Large Wild Animals to Thrive?

The development of ecotourism involving wild animals in Russia is overlooked despite the fact that the country’s territory is significant not only in terms of area but also in terms of the diversity of its flora and fauna. A significant part of Russia’s territory has a low population density, especially beyond the Ural ridge. It retains its natural primeval nature, which can contribute to the development of ecotourism. Initial attempts have been made to develop this, mainly in the European part (Tatarstan, Murmansk Region, the Baltic Sea, Baikal, Altai), but the commercial use of wild animals within ecotourism programs, including the ones in Siberia and the Far East, has not been discussed. This work focuses on the basics of launching ecotourism in the industrial region of Siberia (Kuzbass, Russia) as part of the Alcesalces conservation program.

Roadkills as a Method to Monitor Raccoon Dog Populations

The raccoon dog (Nyctereutes procyonoides) is one of the most frequently killed species on Lithuanian roads. As an invasive species, up-to-date knowledge of population size, trends and spatial distribution is critically important both for species assessment and for the planning of control measures. In Lithuania, however, raccoon dog surveys have not been carried out since 1997. We investigated, therefore, whether roadkill counts on predefined routes could be used as a proxy for a survey. Our dataset includes survey numbers for the period 1956-1997, hunting bag sizes for 1965-2020 (including the spatial distribution of the hunting bag in 2018-2020) and roadkill data relating to 1551 individuals between 2002-2020. At the most local scale, that of the hunting areas of hunting clubs, correlations between the numbers of hunted and roadkilled individuals were negative and insignificant or absent. At the country scale, however, we found significant correlation both between the numbers surveyed and hunted in 1965-1997 (r = 0.88), and between those hunted and the number of roadkills in 2002-2020 (r = 0.56-0.69). Therefore, we consider that roadkill counts on predefined and stable routes may be used as a proxy for a survey at the country scale. Practical implementation of the method is proposed.

Internal transcribed spacer primer evaluation for vascular plant metabarcoding

The unprecedented ongoing biodiversity decline necessitates scalable means of monitoring in order to fully understand the underlying causes. DNA metabarcoding has the potential to provide a powerful tool for accurate and rapid biodiversity monitoring. Unfortunately, in many cases, a lack of universal standards undermines the widespread application of metabarcoding. One of the most important considerations in metabarcoding of plants, aside from selecting a potent barcode marker, is primer choice. Our study evaluates published ITS primers in silico and in vitro, through mock communities and presents newly designed primers. We were able to show that a large proportion of previously available ITS primers have unfavourable attributes. Our combined results support the recommendation of the introduced primers ITS-3p62plF1 and ITS-4unR1 as the best current universal plant specific ITS2 primer combination. We also found that PCR optimisation, such as the addition of 5% DMSO, is essential to obtain meaningful results in ITS2 metabarcoding. Finally, we conclude that continuous quality assurance is indispensable for reliable metabarcoding results.