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The ADnet Bayesian belief network for alder decline: integrating empirical data and expert knowledge
Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal; cE3c - Center for Ecology, Evolution and Environmental Change & CHANGE - Global Change and Sustainability Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.
Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal.
Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal.
Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal.
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2024 (English)In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 947, article id 173619Article in journal (Refereed) Published
Abstract [en]

The globalization in plant material trading has caused the emergence of invasive pests in many ecosystems, such as the alder pathogen Phytophthora ×alni in European riparian forests. Due to the ecological importance of alder to the functioning of rivers and the increasing incidence of P. ×alni-induced alder decline, effective and accessible decision tools are required to help managers and stakeholders control the disease. This study proposes a Bayesian belief network methodology to integrate diverse information on the factors affecting the survival and infection ability of P. ×alni in riparian habitats to help predict and manage disease incidence. The resulting Alder Decline Network (ADnet) management tool integrates information about alder decline from scientific literature, expert knowledge and empirical data. Expert knowledge was gathered through elicitation techniques that included 19 experts from 12 institutions and 8 countries. An original dataset was created covering 1189 European locations, from which P. ×alni occurrence was modeled based on bioclimatic variables. ADnet uncertainty was evaluated through its sensitivity to changes in states and three scenario analyses. The ADnet tool indicated that mild temperatures and high precipitation are key factors favoring pathogen survival. Flood timing, water velocity, and soil type have the strongest influence on disease incidence. ADnet can support ecosystem management decisions and knowledge transfer to address P. ×alni-induced alder decline at local or regional levels across Europe. Management actions such as avoiding the planting of potentially infected trees or removing man-made structures that increase the flooding period in disease-affected sites could decrease the incidence of alder disease in riparian forests and limit its spread. The coverage of the ADnet tool can be expanded by updating data on the pathogen's occurrence, particularly from its distributional limits. Research on the role of genetic variability in alder susceptibility and pathogen virulence may also help improve future ADnet versions.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 947, article id 173619
Keywords [en]
Alnus, Decision support tool, Expert knowledge, Integrated pest management (IPM), Phytophthora ×alni, Riparian ecosystems
National Category
Botany Ecology
Identifiers
URN: urn:nbn:se:umu:diva-227936DOI: 10.1016/j.scitotenv.2024.173619ISI: 001270516700001PubMedID: 38825208Scopus ID: 2-s2.0-85198028751OAI: oai:DiVA.org:umu-227936DiVA, id: diva2:1884809
Available from: 2024-07-18 Created: 2024-07-18 Last updated: 2025-04-24Bibliographically approved

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Jansson, Roland

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