Cranes soar on thermal updrafts behind cold fronts as they migrate across the sea.

Thermal soaring conditions above the sea have long been assumed absent or too weak for terrestrial migrating birds, forcing obligate soarers to take long detours and avoid sea-crossing, and facultative soarers to cross exclusively by costly flapping flight. Thus, while atmospheric convection does develop at sea and is used by some seabirds, it has been largely ignored in avian migration research. Here, we provide direct evidence for routine thermal soaring over open sea in the common crane, the heaviest facultative soarer known among terrestrial migrating birds. Using high-resolution biologging from 44 cranes tracked across their transcontinental migration over 4 years, we show that soaring performance was no different over sea than over land in mid-latitudes. Sea-soaring occurred predominantly in autumn when large water-air temperature difference followed mid-latitude cyclones. Our findings challenge a fundamental migration research paradigm and suggest that obligate soarers avoid sea-crossing not due to the absence or weakness of thermals but due to their low frequency, for which they cannot compensate with prolonged flapping. Conversely, facultative soarers other than cranes should also be able to use thermals over the sea. Marine cold air outbreaks, imperative to global energy budget and climate, may also be important for bird migration.

Simulation applications to support teaching and research in epidemiological dynamics.

BACKGROUND: An understanding of epidemiological dynamics, once confined to mathematical epidemiologists and applied mathematicians, can be disseminated to a non-mathematical community of health care professionals and applied biologists through simple-to-use simulation applications. We used Numerus Model Builder RAMP Ⓡ (Runtime Alterable Model Platform) technology, to construct deterministic and stochastic versions of compartmental SIR (Susceptible, Infectious, Recovered with immunity) models as simple-to-use, freely available, epidemic simulation application programs. RESULTS: We take the reader through simulations used to demonstrate the following concepts: 1) disease prevalence curves of unmitigated outbreaks have a single peak and result in epidemics that 'burn' through the population to become extinguished when the proportion of the susceptible population drops below a critical level; 2) if immunity in recovered individuals wanes sufficiently fast then the disease persists indefinitely as an endemic state, with possible dampening oscillations following the initial outbreak phase; 3) the steepness and initial peak of the prevalence curve are influenced by the basic reproductive value R0, which must exceed 1 for an epidemic to occur; 4) the probability that a single infectious individual in a closed population (i.e. no migration) gives rise to an epidemic increases with the value of R0>1; 5) behavior that adaptively decreases the contact rate among individuals with increasing prevalence has major effects on the prevalence curve including dramatic flattening of the prevalence curve along with the generation of multiple prevalence peaks; 6) the impacts of treatment are complicated to model because they effect multiple processes including transmission, recovery and mortality; 7) the impacts of vaccination policies, constrained by a fixed number of vaccination regimens and by the rate and timing of delivery, are crucially important to maximizing the ability of vaccination programs to reduce mortality. CONCLUSION: Our presentation makes transparent the key assumptions underlying SIR epidemic models. Our RAMP simulators are meant to augment rather than replace classroom material when teaching epidemiological dynamics. They are sufficiently versatile to be used by students to address a range of research questions for term papers and even dissertations.

Disease or drought: environmental fluctuations release zebra from a potential pathogen-triggered ecological trap.

When a transmission hotspot for an environmentally persistent pathogen establishes in otherwise high-quality habitat, the disease may exert a strong impact on a host population. However, fluctuating environmental conditions lead to heterogeneity in habitat quality and animal habitat preference, which may interrupt the overlap between selected and risky habitats. We evaluated spatio-temporal patterns in anthrax mortalities in a plains zebra (Equus quagga) population in Etosha National Park, Namibia, incorporating remote-sensing and host telemetry data. A higher proportion of anthrax mortalities of herbivores was detected in open habitats than in other habitat types. Resource selection functions showed that the zebra population shifted habitat selection in response to changes in rainfall and vegetation productivity. Average to high rainfall years supported larger anthrax outbreaks, with animals congregating in preferred open habitats, while a severe drought forced animals into otherwise less preferred habitats, leading to few anthrax mortalities. Thus, the timing of anthrax outbreaks was congruent with preference for open plains habitats and a corresponding increase in pathogen exposure. Given shifts in habitat preference, the overlap in high-quality habitat and high-risk habitat is intermittent, reducing the adverse consequences for the population.

A versatile web app for identifying the drivers of COVID-19 epidemics.

BACKGROUND: No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. METHODS: Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. RESULTS: We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. CONCLUSION: Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

Drivers of change and stability in the gut microbiota of an omnivorous avian migrant exposed to artificial food supplementation.

Human activities shape resources available to wild animals, impacting diet and probably altering their microbiota and overall health. We examined drivers shaping microbiota profiles of common cranes (Grus grus) in agricultural habitats by comparing gut microbiota and crane movement patterns (GPS-tracking) over three periods of their migratory cycle, and by analysing the effect of artificially supplemented food provided as part of a crane-agriculture management programme. We sampled faecal droppings in Russia (nonsupplemented, premigration) and in Israel in late autumn (nonsupplemented, postmigration) and winter (supplemented and nonsupplemented, wintering). As supplemented food is typically homogenous, we predicted lower microbiota diversity and different composition in birds relying on supplementary feeding. We did not observe changes in microbial diversity with food supplementation, as diversity differed only in samples from nonsupplemented wintering sites. However, both food supplementation and season affected bacterial community composition and led to increased abundance of specific genera (mostly Firmicutes). Cranes from the nonsupplemented groups spent most of their time in agricultural fields, probably feeding on residual grain when available, while food-supplemented cranes spent most of their time at the feeding station. Thus, nonsupplemented and food-supplemented diets probably diverge only in winter, when crop rotation and depletion of anthropogenic resources may lead to a more variable diet in nonsupplemented sites. Our results support the role of diet in structuring bacterial communities and show that they undergo both seasonal and human-induced shifts. Movement analyses provide important clues regarding host diet and behaviour towards understanding how human-induced changes shape the gut microbiota in wild animals.

Rethinking megafauna.

Concern for megafauna is increasing among scientists and non-scientists. Many studies have emphasized that megafauna play prominent ecological roles and provide important ecosystem services to humanity. But, what precisely are 'megafauna'? Here, we critically assess the concept of megafauna and propose a goal-oriented framework for megafaunal research. First, we review definitions of megafauna and analyse associated terminology in the scientific literature. Second, we conduct a survey among ecologists and palaeontologists to assess the species traits used to identify and define megafauna. Our review indicates that definitions are highly dependent on the study ecosystem and research question, and primarily rely on ad hoc size-related criteria. Our survey suggests that body size is crucial, but not necessarily sufficient, for addressing the different applications of the term megafauna. Thus, after discussing the pros and cons of existing definitions, we propose an additional approach by defining two function-oriented megafaunal concepts: 'keystone megafauna' and 'functional megafauna', with its variant 'apex megafauna'. Assessing megafauna from a functional perspective could challenge the perception that there may not be a unifying definition of megafauna that can be applied to all eco-evolutionary narratives. In addition, using functional definitions of megafauna could be especially conducive to cross-disciplinary understanding and cooperation, improvement of conservation policy and practice, and strengthening of public perception. As megafaunal research advances, we encourage scientists to unambiguously define how they use the term 'megafauna' and to present the logic underpinning their definition.

Movement ecology and sex are linked to barn owl microbial community composition.

The behavioural ecology of host species is likely to affect their microbial communities, because host sex, diet, physiology, and movement behaviour could all potentially influence their microbiota. We studied a wild population of barn owls (Tyto alba) and collected data on their microbiota, movement, diet, size, coloration, and reproduction. The composition of bacterial species differed by the sex of the host and female owls had more diverse bacterial communities than their male counterparts. The abundance of two families of bacteria, Actinomycetaceae and Lactobacillaceae, also varied between the sexes, potentially as a result of sex differences in hormones and immunological function, as has previously been found with Lactobacillaceae in the microbiota of mice. Male and female owls did not differ in the prey they brought to the nest, which suggests that dietary differences are unlikely to underlie the differences in their microbiota. The movement behaviour of the owls was associated with the host microbiota in both males and females because owls that moved further from their nest each day had more diverse bacterial communities than owls that stayed closer to their nests. This novel result suggests that the movement ecology of hosts can impact their microbiota, potentially on the basis of their differential encounters with new bacterial species as the hosts move and forage across the landscape. Overall, we found that many aspects of the microbial community are correlated with the behavioural ecology of the host and that data on the microbiota can aid in generating new hypotheses about host behaviour.

Migration, pathogens and the avian microbiome: A comparative study in sympatric migrants and residents.

Animals generally benefit from their gastrointestinal microbiome, but the factors that influence the composition and dynamics of their microbiota remain poorly understood. Studies of nonmodel host species can illuminate how microbiota and their hosts interact in natural environments. We investigated the role of migratory behaviour in shaping the gut microbiota of free-ranging barn swallows (Hirundo rustica) by studying co-occurring migrant and resident subspecies sampled during the autumn migration at a migratory bottleneck. We found that within-host microbial richness (α-diversity) was similar between migrant and resident microbial communities. In contrast, we found that microbial communities (ß-diversity) were significantly different between groups regarding both microbes present and their relative abundances. Compositional differences were found for 36 bacterial genera, with 27 exhibiting greater abundance in migrants and nine exhibiting greater abundance in residents. There was heightened abundance of Mycoplasma spp. and Corynebacterium spp. in migrants, a pattern shared by other studies of migratory species. Screens for key regional pathogens revealed that neither residents nor migrants carried avian influenza viruses and Newcastle disease virus, suggesting that the status of these diseases did not underlie observed differences in microbiome composition. Furthermore, the prevalence and abundance of Salmonella spp., as determined from microbiome data and cultural assays, were both low and similar across the groups. Overall, our results indicate that microbial composition differs between migratory and resident barn swallows, even when they are conspecific and sympatrically occurring. Differences in host origins (breeding sites) may result in microbial community divergence, and varied behaviours throughout the annual cycle (e.g., migration) could further differentiate compositional structure as it relates to functional needs.

Going through the motions: incorporating movement analyses into disease research.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

Making ecological models adequate.

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.