Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

Highly pathogenic avian influenza affects vultures' movements and breeding output

An exceptional highly pathogenic avian influenza (HPAI) outbreak due to H5N1 virus genotypes belonging to clade 2.3.4.4.b has been affecting birds worldwide since autumn 2021.1,2,3 Mortality caused by viral infection has been well documented in poultry and more recently in wild birds, especially in seabird-breeding colonies.4,5,6 However, there is a critical lack of knowledge about how terrestrial birds deal with HPAI virus infections in terms of behavior and space use, especially during the breeding season.7,8,9 Understanding how birds move when they are infected could help evaluate the risk of spreading the virus at a distance among other populations of wild or domestic birds, this latter risk being especially important for commensal bird species. Through long-term GPS tracking, we described the changes in daily movement patterns of 31 adult griffon vultures Gyps fulvus in two French sites in 2022 compared with 3 previous years. In spring 2022, 21 vultures at both sites showed periods of immobility at the nest, during 5.6 days on average. Positive serological status of 2 individuals confirmed that they had been infected by HPAI viruses. Death was recorded for 3 of the 31 tracked individuals, whereas all others recovered and returned quickly to their foraging routine, although at least 9 birds failed breeding. Such immobility patterns and death rates were never observed in previous years and were not related to weather conditions. The high immobility behavior of infected birds could reduce the risks of transmission. The observed vulnerability to HPAI viruses questions the resistance of endangered vulture species worldwide if infected.

Newcastle disease virus transmission dynamics in wild peridomestic birds in the United Arab Emirates

To understand the dynamics of a pathogen in an animal population, one must assess how the infection status of individuals changes over time. With wild animals, this can be very challenging because individuals can be difficult to trap and sample, even more so since they are tested with imperfect diagnostic techniques. Multi-event capture-recapture models allow analysing longitudinal capture data of individuals whose infection status is assessed using imperfect tests. In this study, we used a two-year dataset from a longitudinal field study of peridomestic wild bird populations in the United Arab Emirates during which thousands of birds from various species were captured, sampled and tested for Newcastle disease virus exposure using a serological test. We developed a multi-event capture-recapture model to estimate important demographic and epidemiological parameters of the disease. The modelling outputs provided important insights into the understanding of Newcastle disease dynamics in peridomestics birds, which varies according to ecological and epidemiological parameters, and useful information in terms of surveillance strategies. To our knowledge, this study is the first attempt to model the dynamics of Newcastle disease in wild bird populations by combining longitudinal capture data and serological test results. Overall, it showcased that multi-event capture-recapture models represent a suitable method to analyse imperfect capture data and make reliable inferences on infectious disease dynamics in wild populations.

Uncovering ecological state dynamics with hidden Markov models

Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.