Recent advances in biomedical, biosensor and clinical measurement devices for use in humans and the potential application of these technologies for the study of physiology and disease in wild animals.

The goal of achieving enhanced diagnosis and continuous monitoring of human health has led to a vibrant, dynamic and well-funded field of research in medical sensing and biosensor technologies. The field has many sub-disciplines which focus on different aspects of sensor science; engaging engineers, chemists, biochemists and clinicians, often in interdisciplinary teams. The trends which dominate include the efforts to develop effective point of care tests and implantable/wearable technologies for early diagnosis and continuous monitoring. This review will outline the current state of the art in a number of relevant fields, including device engineering, chemistry, nanoscience and biomolecular detection, and suggest how these advances might be employed to develop effective systems for measuring physiology, detecting infection and monitoring biomarker status in wild animals. Special consideration is also given to the emerging threat of antimicrobial resistance and in the light of the current SARS-CoV-2 outbreak, zoonotic infections. Both of these areas involve significant crossover between animal and human health and are therefore well placed to seed technological developments with applicability to both human and animal health and, more generally, the reviewed technologies have significant potential to find use in the measurement of physiology in wild animals. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Not just a pathogen: The importance of recognizing genetic variability to mitigate a wildlife pandemic.

Emerging infectious diseases (EIDs) are increasingly recognized as a threat to both biodiversity and human health (Scheele et al., 2019; Wells et al., 2020). But pathogens cannot been seen as unique entities; their intraspecific genetic variability represented in variants, strains, antigenic types or genetic lineages may cause different impacts at the population level (Nelson and Holmes, 2007; Greenspan et al., 2018). The global spread of pathogens has been largely facilitated by globalization of transport, which particularly intensified during the last century (O'Hanlon et al., 2018). As seen with SARS-CoV-2, air travel can rapidly spread a pathogen globally (Wells et al., 2020). Furthermore, after initial introduction subsequent translocations of a pathogen may cause the contact of different variants facilitating the rise of novel genotypes that may have higher pathogenicity or transmissibility (Nelson and Holmes, 2007; Greenspan et al., 2018). Chytridiomycosis is an EID caused by the fungus Batrachochytrium dendrobatidis (Bd), that infects amphibian skin causing population declines to extinction in susceptible species. Now a wildlife pandemic, Bd has been recognized as the single pathogen causing the greatest loss of biodiversity on Earth (Scheele et al., 2019). Recent advances in genetics have made novel tools for pathogen detection and characterization more accessible and reliable (Boyle et al., 2004; Byrne et al., 2019). In this issue of Molecular Ecology Resources, Ghosh et al. (2021) report the development of a new genotyping qPCR assay targeting mitochondrial DNA (mtDNA) of Bd, and based on noninvasive swab samples (Figure 1), discriminate between the two most globally widespread and pathogenic genetic lineages of Bd. Having a better understanding of how the genetic diversity of a pathogen is distributed is crucial to understand their spread patterns and develop timely mitigation strategies.

A statistical approach to white-nose syndrome surveillance monitoring using acoustic data.

Traditional pathogen surveillance methods for white-nose syndrome (WNS), the most serious threat to hibernating North American bats, focus on fungal presence where large congregations of hibernating bats occur. However, in the western USA, WNS-susceptible bat species rarely assemble in large numbers and known winter roosts are uncommon features. WNS increases arousal frequency and activity of infected bats during hibernation. Our objective was to explore the effectiveness of acoustic monitoring as a surveillance tool for WNS. We propose a non-invasive approach to model pre-WNS baseline activity rates for comparison with future acoustic data after WNS is suspected to occur. We investigated relationships among bat activity, ambient temperatures, and season prior to presence of WNS across forested sites of Montana, USA where WNS was not known to occur. We used acoustic monitors to collect bat activity and ambient temperature data year-round on 41 sites, 2011-2019. We detected a diverse bat community across managed (n = 4) and unmanaged (n = 37) forest sites and recorded over 5.37 million passes from bats, including 13 identified species. Bats were active year-round, but positive associations between average of the nightly temperatures by month and bat activity were strongest in spring and fall. From these data, we developed site-specific prediction models for bat activity to account for seasonal and annual temperature variation prior to known occurrence of WNS. These prediction models can be used to monitor changes in bat activity that may signal potential presence of WNS, such as greater than expected activity in winter, or less than expected activity during summer. We propose this model-based method for future monitoring efforts that could be used to trigger targeted sampling of individual bats or hibernacula for WNS, in areas where traditional disease surveillance approaches are logistically difficult to implement or because of human-wildlife transmission concerns from COVID-19.