Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.03.16.712014

Dynamics and control of highly pathogenic H5 avian influenza in a threatened pelican population

Abstract

The ongoing epizootic of highly pathogenic avian influenza (HPAI) continues to cause massive deaths in wildlife. Fundamental understanding of its disease ecology in natural populations is urgently needed. This knowledge has been hindered by the difficulty of acquiring data on epidemic dynamics. Here, using data collected from a threatened population of Dalmatian pelicans (Pelecanus crispus), we recover the epidemiological and evolutionary history of one of the largest HPAI wildlife mortality events. The results show that this devastating outbreak was likely seeded by a single introduction associated with movement of the species. By estimating epidemiological features of two consecutive outbreaks in the same population, we show that panzootic H5N1 since 2022 likely exhibits higher transmissibility and longer shedding time in non-reservoir birds, compared to previous H5NX subtypes. We also evaluate effectiveness of past and future control measures: carcass removal during the outbreak is shown to have surprisingly little impact on mitigating the mortality; and current H5 vaccines relying on capture and injection to deliver cannot establish herd immunity in a wildlife population. The results provide the first field evidence supporting the hypothesis that viral fitness difference of H5N1 to previous H5NX subtypes is the key cause of the expanded epizootic and panzootic since 2022, and on highly debated HPAI management strategies in wildlife populations. Author SummarySince late 2021, a panzootic of H5N1 highly pathogenic avian influenza (HPAI) has caused unprecedented mass mortality in wildlife. Many severely affected species are critical for ecosystem functions, including several threatened and endangered species. However, fundamental knowledge of HPAI disease ecology in natural populations is still lacking, and the effectiveness of potential controls is under debate. Here, using data collected from one of the largest HPAI outbreak in wild animals - over 1700 deaths (80% of the population) in a threatened population of Dalmatian pelicans in Greece, for the first time we recover the transmission dynamics of H5N1 in a migratory bird population. Based on the recovered dynamics, we show that removing carcasses during the outbreak was surprisingly ineffective, and future potential vaccination would require a novel delivery method to establish population immunity in wildlife. Our study provides new insight in the epidemiology of HPAI clade 2.3.4.4b in wildlife, and provides a foundation for assessing interventions within this complicated system.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yang, Q., Alexandrou, O., Höfle, U., Minayo-Martin, S., Chaintoutis, S. C., Moutou, E., Dovas, C. I., Moncla, L. H., Grenfell, B. T., Catsadorakis, G.. 2026-03-18. Dynamics and control of highly pathogenic H5 avian influenza in a threatened pelican population. https://doi.org/10.64898/2026.03.16.712014

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗