New algorithm will combat invasive species around the world
Hebrew University's development is particularly timely, as invasive species continue to pose increasing threats to biodiversity and the global economy.
Due to global change, many species have become established outside their native habitats. Now, an innovative computer algorithm that shows how to significantly enhance the management of invasive species – offering a cost-effective solution for allocating resources across diverse locations for safeguarding ecosystems, agriculture, and public health – has been developed at the Hebrew University of Jerusalem (HU).
This advancement can support policymakers and conservationists in addressing the growing threats posed by invasive species to biodiversity and the global economy, said Prof. Adam Lampert from the Institute of Environmental Sciences at the Robert H Smith Faculty of Agriculture, Food and Environment who headed the team.
The new study, just published in the journal PLOS Computational Biology under the title “Optimizing strategies for slowing the spread of invasive species” is adaptable to a wide range of population dynamical models and treatment methods. It also determines the most effective spatial distribution of treatment efforts to slow the propagation speed of non-native species – birds, insects, and other creatures – that are taking over environments of native ones.
Threats to ecosystems
“In light of the global spread of invasive species that threaten ecosystems, biodiversity, agriculture, and human health, we developed an advanced computer algorithm to identify the optimal strategy to slow the spread of established invaders,” he wrote.
“Our findings are a promising advancement in environmental management practices,” Lampert continued. “The algorithm was developed for both general models and a model that is more specific for the spongy moth in North America, demonstrating its generality and potential to improve current strategies significantly.”
The research focused on two models: a broad-based generic model and a detailed model tailored to the spongy moth using mating disruption techniques. The results highlighted that utilizing this novel algorithm allows improving the cost-efficiency of treatment strategies.
This development is particularly timely, as invasive species continue to pose increasing threats to biodiversity and the global economy. By improving how treatment efforts are distributed in combatting these species, the algorithm can support policymakers and conservationists in their ongoing efforts to safeguard environmental health.
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