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A novel integrated framework to identify and characterize regional-scale pest insect dispersal

Dargent, F.; Reich, M. S.; Miller, M.; Studens, K.; Benvidi, N.; Perrault, K.; Aibueku, J.; Holmes, B.; Bataille, C.; Candau, J.-N.

2025-03-01 ecology
10.1101/2025.02.26.640127 bioRxiv
Show abstract

Forest pest insects cause major socio-economic impacts, global losses of millions of dollars, and ecosystem changes. A key challenge for their management is tracing regional dispersal events critical to outbreak dynamics. We developed an integrated tracing framework for pest insects by combining isotope geolocation, ecological data, and atmospheric modeling, and applied this framework to the eastern spruce budworm moth (Choristoneura fumiferana), the most severe defoliator of the North American boreal forest, to trace outbreak dispersal events. We first generated a North American model of bioavailable sulfur isotope ({delta}34S) variation in space (isoscape), which predominantly varied in response to oceanic sulfate deposition, and then calibrated it to spruce budworm tissues of known origin. We used an automated trap network with high temporal resolution to collect samples and identify potential immigration events of eastern spruce budworm to Nova Scotia, Canada. We traced the natal origin of these immigrants by integrating high-probability regions derived from{delta} 34S probabilistic assignments and HYSPLIT atmospheric dispersal models. Since high larval density is a strong predictor of budworm defoliation and emigration, HYSPLIT atmospheric dispersal models, which integrated spruce budworm behavioral constraints (e.g., flight velocity, altitude, and temperature thresholds), were started from defoliated areas to narrow-down the area of natal origin and estimate the migration route. We find that this integrated framework allows to narrow down the region of pest origins, restricting it to a few possible locations and demonstrating long-distance dispersal of spruce budworm across [~]400Km over the Gulf of St. Lawrence. Our framework demonstrates the utility of{delta} 34S geolocation in insect tracing, and that combining isotopic data with ecological indicators and atmospheric modeling offers an unprecedented resolution in understanding insect dispersal ecology. The approach is transferable to trace other migratory insect species to address conservation, agriculture, and bio-surveillance needs in the context of global environmental change.

Published in Ecological Applications (predicted rank #4) · training set

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