Invasive forest pests, forest health, early warning, pest spread modelling, forest surveillance, spatial networks, remote sensing, climate resilience, decision support
TOPIC 1 – Climate-Resilient Forest Systems
We are developing a project proposal for the FOREST Joint Call 2026 to support the early warning and surveillance of invasive forest pests across Europe and Türkiye.
The proposed project will investigate how invasive pests spread between forest areas and how surveillance can be prioritised when observations are limited. We aim to develop a spatial, network-based modelling approach combining pest occurrence records, host-tree distribution and, where available, satellite observations. The model will consider natural spread, human-mediated transport and uncertainty about the initial source of an invasion.
The oak lace bug (Corythucha arcuata) is being considered as a potential case study. We are interested in exploring how environmental and climate conditions influence pest spread and forest vulnerability, with the research questions and case studies to be developed jointly with partners.
We are looking for universities, research institutes and forest management organisations with expertise in forest entomology, invasive species, forest ecology, climate impacts, spatial modelling, remote sensing or forest surveillance. Partners able to contribute pest occurrence and host-tree data, ecological expertise, case-study areas, field validation or connections with forest managers would be particularly welcome.
We are interested in jointly developing the proposal and establishing a transnational consortium for the FOREST Joint Call 2026.
Maltepe University is a foundation university located in Istanbul, Türkiye. The Department of Software Engineering, within the Faculty of Engineering and Natural Sciences, conducts education and research in software development, artificial intelligence and data-driven applications.
Our team can contribute to the proposed project through expertise in software engineering, data analysis, machine learning, network-based modelling and the development of decision-support tools. We are interested in working with forest researchers and practitioners to develop and evaluate an open-source tool that can support forest pest surveillance and early warning.
