Drivers of host-infectious agent community associations in seabirds from sub-Antarctic oceanic islands

Bralet, T.; Lejeune, M.; Tornos, J.; Augustin, C.; Galon, C.; Berland, F.; Mokorel-Pouye, A.; Gamble, A.; Moutailler, S.; Aaziz, R.; Laroucau, K.; Boulinier, T.

doi:10.64898/2026.02.23.706941

bioRxiv · 10.64898/2026.02.23.706941

Drivers of host-infectious agent community associations in seabirds from sub-Antarctic oceanic islands

Bralet, T.·Lejeune, M.·Tornos, J.·Augustin, C.·Galon, C.·Berland, F.·Mokorel-Pouye, A.·Gamble, A.·Moutailler, S.·Aaziz, R.·Laroucau, K.·Boulinier, T.

View original source ↗Export metadata ↓

Abstract

Understanding the patterns and drivers of infectious disease dynamics at different levels of life organization is a fundamental disease ecology challenge. In this study, we performed a multi-infectious agents (IA) screening of 1,983 individuals belonging to 18 seabird species, sampled between 2017 and 2022 across five sub-Antarctic islands in the Southern Indian and Atlantic Oceans. Our aim was to identify the drivers of IA community in these seabirds by considering intrinsic and extrinsic factors on three dimensions: distance between seabird metapopulations and within-island sites, macro-community (host functional traits based on feeding and breeding features) and intra-host IA community composition. Samples were screened for 24 DNA-based IAs using a high-throughput real-time PCR method, followed by subsequent hierarchical modelling of species communities. Campylobacter lari, Escherichia coli, Pasteurella multocida, Chlamydiaceae and Mycobacterium spp. were found to be present on all islands in some, but not all, species. However, only E. coli was detected in all species. According to our modelling results, the influence of year and within-island site had limited influence on IA presence/absence, accounting respectively for an average of 1.0% and 1.8% of the variation in IA community. Including host species as a response variable produced a better model fit than including host species functional traits, highlighting the difficulty in simplifying systems by focusing on high-level functional categories. Model outputs show how each species influences IA distribution depending on IA traits, but no clear trend has emerged. Interestingly, burrowing species were less frequently infected with directly transmitted IAs, probably due to the low frequency of transmission events associated with their breeding features and limited social contacts. Our findings pave the way for further multi-dimension studies that would combine complementary approaches to disentangle the complex processes at play in the dynamics of hosts and IAs interactions.

Explore related subjects

ecology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Bralet, T., Lejeune, M., Tornos, J., Augustin, C., Galon, C., Berland, F., Mokorel-Pouye, A., Gamble, A., Moutailler, S., Aaziz, R., Laroucau, K., Boulinier, T.. 2026-02-24. Drivers of host-infectious agent community associations in seabirds from sub-Antarctic oceanic islands. https://doi.org/10.64898/2026.02.23.706941

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

KEEP EXPLORING

Related preprints

bioRxiv2026

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↗

bioRxiv2026

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↗

bioRxiv2026

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↗