Supplementary material for "Evaluating the demographic impacts of the Highly Pathogenic Avian Influenza panzootic"

Supplementary material for "Evaluating the demographic impacts of the Highly Pathogenic Avian Influenza panzootic" Jaume A. Badia-Boher*, Michael Schaub*, Martin Mollet, Peter van Geneijgen, Henk P. van der Jeugd, Valentina Caliendo, Marc Kéry* *Schweizerische Vogelwarte, 6204 Sempach, Switzerland ORCID: Jaume A. Badia-Boher: 0000-0003-2162-6355 Michael Schaub: 0000-0003-2745-0557 Henk P. van der Jeugd: 0000-0002-5332-9526 Marc Kéry: 0000-0001-7476-7616 Abstract 1. The spread of a new Highly Pathogenic Avian Influenza virus (HPAIV-H5N1) has, since 2021, triggered one of the most severe wildlife panzootic ever reported, with suspected population crashes in hundreds of bird and mammal species. However, to date no studies have evaluated the demographic mechanisms underlying these declines. 2. We used integrated population modeling (IPM) along with Bayesian population forecasting and resilience analysis, to evaluate the impact of HPAIV-H5N1 on age-structured survival, productivity, stage-specific population dynamics, and demographic resilience in a long-lived bird, the peregrine falcon (Falco peregrinus), in the Netherlands. 3. Our analyses revealed drastic declines in adult survival – the key driver of population dynamics in a long-lived species – in 2022 and 2023, coinciding with a ~25% decline in breeding pairs. This suggests a shift in HPAIV dynamics compared to previous epizootics, with recurrent outbreaks during consecutive seasons and years, resulting in potentially stronger population impacts. 4. Resilience analysis revealed that HPAIV outbreaks could cause long-lasting demographic impacts in the population. The breeding population could take a decade to recover to pre-panzootic levels and may ultimately stabilize at ~21% below its former size. Recovery could take longer if HPAIV outbreaks become more frequent in the future, which is likely under current epidemiological predictions. 5. Synthesis and applications. Our findings demonstrate that the HPAIV panzootic can cause rapid and long-lasting impacts on the dynamics of long-lived species. This raises major concerns for the conservation and long-term viability of the many severely acected long-lived species worldwide. As wildlife disease is predicted to become a leading cause of biodiversity loss, a global-scale conservation strategy is urgently needed, which includes improved management, surveillance, and applied research ecorts. Our combined use of IPMs, forecasts, and resilience analyses also serves as a benchmark for evaluating the ecects not only of zoonotics, but also of any other type of disturbance in wild populations. Readme R code and data to reproduce all the analyses presented in the paper. There are seven files: 1. Code_HPAI.R - Code to run the model with data from 1993 to 2024 (i.e., including HPAI panzootic outbreaks) + projections until 2034 2. data_hpai.RData - Necessary data to run the model with data from 1993 to 2024 (i.e., including HPAI panzootic outbreaks) + projections until 2034 3. pva_hpai.RData – Results of the model with data from 1993 to 2024 (i.e., including HPAI panzootic outbreaks) + projections until 2034 4. Code_NoHPAI.R - Code to run the model with data from 1993 to 2020 (i.e., excluding HPAI panzootic outbreaks) + projections until 2034 5. data_nohpai.RData - Necessary data to run the model with data from 1993 to 2020 (i.e., excluding HPAI panzootic outbreaks) + projections until 2034 6. pva_nohpai.RData - Results of the model with data from 1993 to 2020 (i.e., excluding HPAI panzootic outbreaks) + projections until 2034 7. Plots_Publish.R – Code to run the plots that appear in the manuscript. The code runs with the .RData results files (numbers 3 and 6). Instructions: Download all files in the same folder and set it as a working directory. The code is set to find and load the data from the working directory. Key words Avian Influenza, Integrated Population Modelling, Wildlife Disease, Demographic Analysis, Demographic Resilience, Long-lived Species, Capture-Mark-Recapture, Wildlife Epizootics, Population Models, Peregrine Falcon