Bio-telemetry from small tags attached to animals is one of the principal methods for studying the ecology and behaviour of wildlife. The field has constantly evolved over the last 80 years as technological improvement enabled a diversity of sensors to be integrated into the tags (e.g., GPS, accelerometers, etc.). However, retrieving data from tags on free-ranging animals remains a challenge since satellite and GSM networks are relatively expensive and or power hungry. Recently a new class of low-power communication networks have been developed and deployed worldwide to connect the internet of things (IoT). Here, we evaluated one of these, the Sigfox IoT network, for the potential as a real-time multi-sensor data retrieval and tag commanding system for studying fauna across a diversity of species and ecosystems. We tracked 312 individuals across 30 species (from 25 g bats to 3 t elephants) with seven different device concepts, resulting in more than 177,742 successful transmissions. We found a maximum line of sight communication distance of 280 km (on a flying cape vulture [Gyps coprotheres]), which sets a new documented record for animal-borne digital data transmission using terrestrial infrastructure. The average transmission success rate amounted to 68.3% (SD 22.1) on flying species and 54.1% (SD 27.4) on terrestrial species. In addition to GPS data, we also collected and transmitted data products from accelerometers, barometers, and thermometers. Further, we assessed the performance of Sigfox Atlas Native, a low-power method for positional estimates based on radio signal strengths and found a median accuracy of 12.89 km (MAD 5.17) on animals. We found that robust real-time communication (median message delay of 1.49 s), the extremely small size of the tags (starting at 1.28 g without GPS), and the low power demands (as low as 5.8 µAh per transmitted byte) unlock new possibilities for ecological data collection and global animal observation.

Sex‐based differences in physiology, behaviour and demography commonly result in differences in disease prevalence. However, sex differences in prevalence may reflect exposure rather than transmission, which could affect disease control programmes. One potential example is chronic wasting disease (CWD), which has been observed at greater prevalence among male than female deer. We used an age‐ and sex‐structured simulation model to explore harvest‐based management of CWD under three different transmission scenarios that all generate higher male prevalence: (1) increased male susceptibility, (2) high male‐to‐male transmission or (3) high female‐to‐male transmission. Both female and male harvests were required to limit CWD epidemics across all transmission scenarios (approximated by R₀), though invasion was more likely under high female‐to‐male transmission. In simulations, heavily male‐biased harvests controlled CWD epidemics and maintained large host populations under high male‐to‐male transmission and increased male susceptibility scenarios. However, male‐biased harvests were ineffective under high female‐to‐male transmission. Instead, female‐biased harvests were able to limit disease transmission under high female‐to‐male transmission but incurred a trade‐off with smaller population sizes. Synthesis and applications. Higher disease prevalence in a sex or age group may be due to higher exposure or susceptibility but does not necessarily indicate if that group is responsible for more disease transmission. We showed that multiple processes can result in the pattern of higher male prevalence, but that population‐level management interventions must focus on the sex responsible for disease transmission, not just those that are most exposed.

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age‐specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late‐stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade‐offs from a management perspective—that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Moose populations in the northeastern United States have declined over the past 15 years, primarily due to the impacts of winter ticks. Research efforts have focused on the effects of winter tick infestation on moose survival and reproduction, but stress and nutritional responses to ticks and other stressors remain understudied. We examined the influence of several environmental factors on moose calf stress hormone metabolite concentrations and nutritional restriction in Vermont, USA. We collected 407 fecal and 461 snow urine samples from 84 radio-collared moose calves in the winters of 2017–2019 (January–April) to measure fecal glucocorticoid metabolites (fGCM) concentrations and urea nitrogen:creatinine (UN:C) ratios. We used generalized mixed-effects models to evaluate the influence of individual condition, winter ticks, habitat, climate and human development on stress and nutrition in calf moose. We then used these physiological data to build generalized linear models to predict calf winter survival. Calf fGCM concentrations increased with nutritional restriction and snow depth during adult winter tick engorgement. Calf UN:C ratios increased in calves with lighter weights and higher tick loads in early winter. Calf UN:C ratios also increased in individuals with home ranges composed of little deciduous forests during adult winter tick engorgement. Our predictive models estimated that winter survival was negatively related to UN:C ratios and positively related to fGCM concentrations, particularly in early winter. By late March, as winter ticks are having their greatest toll and endogenous resources become depleted, we estimated a curvilinear relationship between fGCM concentrations and survival. Our results provide novel evidence linking moose calf stress and nutrition, a problematic parasite and challenging environment and winter survival. Our findings provide a baseline to support the development of non-invasive physiological monitoring for assessing environmental impacts on moose populations.

The niche is a key concept that unifies ecology and evolutionary biology. However, empirical and theoretical treatments of the niche are mostly performed at the species level, neglecting individuals as important units of ecological and evolutionary processes. So far, a formal mathematical link between individual-level niches and higher organismal-level niches has been lacking, hampering the unification of ecological theories and more accurate forecasts of biodiversity change. To fill in this gap, we propose a bottom–up approach to derive population and higher organismal-level niches from individual niches. We demonstrate the power of our framework by showing that 1) the statistical properties of higher organismal-level niches (e.g., niche breadth, skewness, etc.) can be partitioned into individual contributions and 2) the species-level niche shifts can be estimated by tracing the responses of individuals. By using individual-level GPS (Global Positioning System) tracking data from three different species, we show that climate change could have contrasting consequences on population-level niche shift depending on individual niche compositions. Our method paves the way for a unifying niche theory and enables mechanistic assessments of organism–environment relationships across organismal scales.

SARS-CoV-2 spreads quickly in dense populations, with serious implications for universities, workplaces, and other settings where exposure reduction practices are difficult to implement. Rapid screening has been proposed as a tool to slow the spread of the virus; however, many commonly used diagnostic tests (e.g., RT-qPCR) are expensive, difficult to deploy (e.g., require a nasopharyngeal specimen), and have extended turn-around times. We evaluated testing regimes that combined diagnostic testing using qPCR with high-frequency screening using a novel reverse-transcription loop-mediated isothermal amplification (RT-LAMP, herein LAMP) assay. We used a compartmental susceptible-exposed-infectious-recovered (SEIR) model to simulate screening of a university population. We also developed a Shiny application to allow administrators and public health professionals to develop optimal testing strategies given site-specific assumptions about testing investment, target population, and cost. The frequency of screening, especially when pooling samples, was more important for minimizing epidemic size than test sensitivity, behavioral compliance, contact tracing capacity, and time between testing and results. Our results suggest that when testing budgets are limited, it is safer and more cost-effective to allocate the majority of funds to screening. Rapid, cost-effective, and scalable screening tests, like LAMP, should be viewed as critical components of SARS-CoV-2 testing in high-density populations.