RT Journal Article
T1 How Accurately Do Species Distribution Models Predict the Expansion of Invasive Insects, and Does Climate Data Choice Matter? Insights From the Invasion of Dryocosmus kuriphilus
A1 Pérez Girón, José Carlos
A1 Castedo Dorado, Fernando
A1 Lombardero, María Josefa
A1 Álvarez Álvarez, Pedro
K1 Biological invasions
K1 Chestnut gall wasp
K1 Ecological modelling
K1 Habitat suitability
K1 Invasive pest
K1 Species distribution models
AB Species distribution models (SDMs) are widely used to predict the spread of invasive species, yet their accuracy over time and the influence of climate data resolution remain unclear. Here, we examine the capacity of SDMs to predict the distribution and short-term expansion of the invasive gall wasp Dryocosmus kuriphilus, and compare the performance of locally sourced, annually resolved climate data with global climatic datasets. We modelled the annual distribution and expansion of D. kuriphilus using SDMs. Three climate datasets: regional interpolations, temporally explicit CHELSA time series, and long-term CHELSA averages, were compared to test their influence on model accuracy. Habitat suitability was estimated with GLM, MaxEnt, and Random Forest, and model performance was evaluated with the Boyce index. Temporal transferability was assessed by projecting early-year models to subsequent years and analysing accuracy patterns via multifactorial ANOVA. Model accuracy, measured with the Boyce index, improved over time, surpassing 0.7 for most datasets and algorithms. Differences among climate datasets were minor, although regional data slightly enhanced early predictions. MaxEnt consistently achieved the highest and most stable performance, while Random Forest and GLM were more variable. Models projected beyond their calibration years rapidly lost predictive power, showing limited temporal transferability. ANOVA confirmed that neither climate dataset nor algorithm significantly influenced accuracy, though MaxEnt performed best overall. SDMs are valuable tools for invasive species management when used adaptively. Accuracy improves with more occurrence data but declines when projecting future expansion, highlighting the need for regular updates. Incorporating fine-scale, temporally explicit data enhances early detection, monitoring, and rapid-response interventions. Continuously updated SDMs support timely management decisions, including invasion monitoring, prioritisation of actions, and integration of biological control or other mitigation strategies.
PB Wiley
YR 2026
FD 2026
LK https://hdl.handle.net/10272/28201
UL https://hdl.handle.net/10272/28201
LA eng
NO Pérez-Girón, J. C., F.Castedo-Dorado, M. J.Lombardero, and P.Álvarez-Álvarez. 2026. “How Accurately Do Species Distribution Models Predict the Expansion of Invasive Insects, and Does Climate Data Choice Matter? Insights From the Invasion of Dryocosmus kuriphilus.” Journal of Applied Entomology1–12. https://doi.org/10.1111/jen.70103.
NO Funding for open access charge: Universidad de Huelva / CBUA
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026