The Internet of Animals: what it is, what it could be

The potential of remote sensing for improved infectious disease ecology research and practice

Outbreaks of COVID-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of infectious diseases on public health, ecosystems and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we (i) conduct a review of primary literature to quantify current uses of remote sensing in disease ecology; and (ii) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are enabling early detection of forest pathogens and that satellite data are most commonly used to study geographically widespread human diseases. Opportunities remain for increased use of data products that characterize vegetation, surface water and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal telemetry can support decision-making for disease management by providing insights into wildlife disease dynamics. Integrating these opportunities will advance both research and management of infectious diseases.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics. However, these movements remain understudied, particularly in the mid-latitudes, where many Arctic breeders overwinter, increasing uncertainty in their ranges and numbers. Here, we show that the Arctic breeding raptor Rough-legged buzzard, which overwinters in the mid-latitudes, has a specific wintering strategy. After migrating ca. 1500 km from the Arctic to mid-latitudes, the birds continue to move throughout the entire over-wintering period, traveling another 1000 km southwest and then back northeast as the snowline advances. This continuous movement makes their wintering range dynamic throughout the season. In essence, this movement represents an extension of the quick migration process, albeit at a slower pace, and we have termed this migration pattern 'foxtrot migration', drawing an analogy to the alternating fast and slow movements of the foxtrot dance. These results highlight the potential errors in range mapping from single mid-winter surveys and emphasize the importance of this migration pattern in assessing the conservation status of bird species. Understanding this migration pattern could help to correctly estimate bird populations in over-wintering ranges, which is especially important for species that nest in hard-to-reach regions such as the Arctic.

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals

Studying animal social systems requires understanding variations in contact and interaction, influenced by factors like environmental conditions, resource availability, and predation risk. Traditional observational methods have limitations, but advancements in sensor technologies and data analytics provide new opportunities. We developed a wireless wearable sensor system, "Juxta," with features such as modular battery packs and a smartphone app for data collection. A pilot study on free-living prairie voles (Microtus ochrogaster), a species with complex social behavior, demonstrated Juxta's potential for studying social networks and behavior. We propose a framework for merging temporal, spatial, and event-driven data, which can help explore complex social dynamics across species and environments.

Emerging technologies in citizen science and potential for insect monitoring

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

The spatiotemporal context of individual specialization in resource use and environmental associations

1. Individual niche specialization is widespread in natural populations and has key implications for higher levels of biological organization. This phenomenon, however, has been primarily quantified in resource niche axes, overlooking individual variation in environmental associations (i.e. abiotic conditions organisms experience). 2. Here, we explore what we can learn from a multidimensional perspective of individual niche specialization that integrates resource use and environmental associations into a common framework. 3. By combining predictions from theory and simple simulations, we illustrate how (i) multidimensional intraspecific niche variation and (ii) the spatiotemporal context of interactions between conspecifics scale up to shape emergent patterns of the population niche. 4. Contemplating individual specialization as a multidimensional, unifying concept across biotic and abiotic niche axes is a fundamental step towards bringing this concept closer to the n-dimensional niche envisioned by Hutchinson.

Edge computing in wildlife behavior and ecology

Modern sensor technologies increasingly enrich studies in wildlife behavior and ecology. However, constraints on weight, connectivity, energy and memory availability limit their implementation. With the advent of edge computing, there is increasing potential to mitigate these constraints, and drive major advancements in wildlife studies.

A Wireless Wearable Ecosystem for Social Network Analysis in Free-living Animals

Understanding the dynamics of animal social systems requires studying variation in contact and interaction, which is influenced by environmental conditions, resource availability, and predation risk, among other factors. Traditional (direct) observational methods have limitations, but advancements in sensor technologies and data analytics provide unprecedented opportunities to study these complex systems in naturalistic environments. Proximity logging and tracking devices, capturing movement, temperature, and social interactions, offer non-invasive means to quantify behavior and develop empirical models of animal social networks. However, challenges remain in integrating different data types, incorporating more sensor modalities, and addressing logistical constraints. To address these gaps, we developed a wireless wearable sensor system with novel features (called "Juxta"), including modular battery packs, memory management for combining data types, reconfigurable deployment modes, and a smartphone app for data collection. We present data from a pilot study on prairie voles ( Microtus ochrogaster ), which is a small mammal species that exhibits relatively complex social behavior. We demonstrate the potential for Juxta to increase our understanding of the social networks and behavior of free-living animals. Additionally, we propose a framework to guide future research in merging temporal, spatial, and event-driven data. By leveraging wireless technology, battery efficiency, and smart sensing modalities, our wearable ecosystem offers a scalable solution for real-time, high-resolution data capture and analysis in animal social network studies, opening new avenues for exploring complex social dynamics across species and environments.

Accelerometer-based detection of African swine fever infection in wild boar

Infectious wildlife diseases that circulate at the interface with domestic animals pose significant threats worldwide and require early detection and warning. Although animal tracking technologies are used to discern behavioural changes, they are rarely used to monitor wildlife diseases. Common disease-induced behavioural changes include reduced activity and lethargy ('sickness behaviour'). Here, we investigated whether accelerometer sensors could detect the onset of African swine fever (ASF), a viral infection that induces high mortality in suids for which no vaccine is currently available. Taking advantage of an experiment designed to test an oral ASF vaccine, we equipped 12 wild boars with an accelerometer tag and quantified how ASF affects their activity pattern and behavioural fingerprint, using overall dynamic body acceleration. Wild boars showed a daily reduction in activity of 10-20% from the healthy to the viremia phase. Using change point statistics and comparing healthy individuals living in semi-free and free-ranging conditions, we show how the onset of disease-induced sickness can be detected and how such early detection could work in natural settings. Timely detection of infection in animals is crucial for disease surveillance and control, and accelerometer technology on sentinel animals provides a viable complementary tool to existing disease management approaches.