Technological advances in field studies of pollinator ecology and the future of e-ecology.

Our review looks at recent advances in technologies applied to studying pollinators in the field. These include RFID, radar and lidar for detecting and tracking pollinators; wireless sensor networks (e.g. 'smart' hives); automated visual and audio monitoring systems including vision motion software for monitoring fine-scale pollinator behaviours over extended periods; and automated species identification systems based on machine learning that can vastly reduce the bottleneck in (big) data analysis. An improved e-ecology platform that leverages these tools is needed for ecologists to acquire and understand large spatiotemporal datasets, and thus inform knowledge gaps in environmental policy-making. Developing the next generation of e-ecology tools will require synergistic partnerships between academia and industry and significant investment in a cross-disciplinary scientific consortia.

A prototype RFID tag for detecting bumblebee visitations within fragmented landscapes.

Detecting the arbitrary movements of fast-moving insects under field conditions is notoriously difficult because existing technologies are limited by issues of size, weight, range and cost. Here, we establish proof-of-concept for a prototype long-range, passive radio frequency identification (RFID) tagging system for detecting bumblebees and similar sized insects. The prototype tags, weighing 81 mg (49% of mean bee body weight), were flown by bumblebees in a glasshouse and detected at a distance of 1.5 m from a 2 W UHF reader with two aerials. This detection distance is two orders of magnitude greater than existing RFID tags that can be flown by medium-sized bees and, thus, is a significant breakthrough for insect tracking that could be applied to plant conservation and restoration efforts in fragmented landscapes. Proof-of-concept has been successfully established and, with further development, we are likely to optimize the system by reducing tag size and weight to limit effects on bee behaviour, and by increasing the detection distance. We envisage the production system being used to detect and track bee movement pathways within a designed network of field-deployed low-cost readers and aerials. The production system could be used in a wide variety of scientific and commercial applications.

Using SDS-PAGE gel fingerprinting to identify soft-bodied wood-boring insect larvae to species.

BACKGROUND: This paper describes the progress that we have made in assessing the feasibility of 'fingerprinting' using imaged SDS-PAGE gels of haemolymph proteins, to identify soft-bodied wood-boring insect larvae such as the Asian longhorn beetle, Anoplophora glabripennis (Motscholsky, 1853) (Coleoptera: Cerambycidae). Because of stringent import restrictions and difficulty in obtaining licences to work with these organisms, we opted to work with four species of scarab beetle, Mecynorhina polyphemus (Fabricius, 1781), Pachnoda sinuata (Fabricius, 1775), Eucidella shiratica (Csiki, 1909) and Eucidella shultzeorum (Kolbe, 1906) which have near identical larval morphologies. RESULTS: We show that this technology when combined with an advanced pattern matching system (Digital Automated Identification SYstem - DAISY) can classify soft-bodied insect larvae that are almost identical morphologically to species at a level of accuracy is in excess of 98%. The study also indicates that the technology copes well with noisy data and small training sets. CONCLUSION: The experience gained in undertaking this study gives us confidence that we will be able to develop a field deployable system in the medium term. We believe that as a high-throughput identification tool, this technology is superior to competitor technologies (e.g. fingerprinting of imaged DNA gels) in terms of speed, cost and ease of use; and therefore, is suitable for low-cost deployment in the field. © 2017 Society of Chemical Industry.

Automated species identification: why not?

Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recognizing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than practical constraints on what might possibly be achieved.

Tools and approaches for the construction of knowledge models from the neuroscientific literature.

Within this paper, we describe a neuroinformatics project (called "NeuroScholar," http://www.neuroscholar.org/) that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the "View-Primitive-Data Model framework," [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.