Evaluating invasion risk and population dynamics of the brown marmorated stink bug across the contiguous United States

Abstract

BACKGROUND Invasive species threaten the productivity and stability of natural and managed ecosystems. Predicting the spread of invaders, which can aid in early mitigation efforts, is a major challenge, especially in the face of climate change. While ecological niche models are effective tools to assess habitat suitability for invaders, such models have rarely been created for invasive pest species with rapidly expanding ranges. Here, we leveraged a national monitoring effort from 543 sites over 3 years to assess factors mediating the occurrence and abundance of brown marmorated stink bug (BMSB, Halyomorpha halys), an invasive insect pest that has readily established throughout much of the United States. RESULTS We used maximum entropy models to estimate the suitable habitat of BMSB under several climate scenarios, and generalized boosted models to assess environmental factors that regulated BMSB abundance. Our models captured BMSB distribution and abundance with high accuracy, and predicted a 70% increase in suitable habitat under future climate scenarios. However, environmental factors that mediated the geographical distribution of BMSB were different from those driving abundance. While BMSB occurrence was most affected by winter precipitation and proximity to populated areas, BMSB abundance was influenced most strongly by evapotranspiration and solar photoperiod. CONCLUSION Our results suggest that linking models of establishment (occurrence) and population dynamics (abundance) offers a more effective way to forecast the spread and impact of BMSB and other invasive species than simply occurrence‐based models, allowing for targeted mitigation efforts. Implications of distribution shifts under climate change are discussed. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Temperature-Dependent Life Table Parameters of Brown Marmorated Stink Bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) in the United States

“…Adult longevity of H. halys from the current study falls within the lower range when compared to other studies [ 4 , 14 , 15 ]. Such differences might depend on factors including diet, adaptation of species to certain climatic conditions, and genetic background [ 24 , 25 , 26 , 27 ]. The data from the current study are however comparatively similar and within realistic developmental parameters.…”

“…These comparative and unique parameters of H. halys can be used to explore possible scenarios at a range of temperature extremes and help researchers in the future to strategize their pest control methods. Population distribution and seasonal estimation models have been developed [ 9 , 27 ] for H. halys but can likely be elaborated and refined with the addition of the parameters from these studies.…”

Suitability of the shrub-steppe habitat of Eastern Washington for Halyomorpha halys (Hemiptera: Pentatomidae)

“…While these models helped with our understanding of potential spread on a global scale, they did not give sufficiently detailed predictions for integrated pest management purposes as correlations between output scores from ENMs and mean abundance have been inconsistent or weak (VanDerWal et al 2009, Osorio-Olvera et al 2019). A newer approach for H. halys (Illán et al 2022) has been to combine the use of an ENM to predict and identify the factors mediating occurrence with a generalized boosted model to predict and identify factors mediating abundance. This approach demonstrated that the factors that are most influential for the former need not be the same as for the latter.…”

Modeling the pest‐pathogen threats in a warming world for the red turpentine beetle (Dendroctonus valens) and its symbiotic fungus (Leptographium procerum)

“…35 The maximum entropy algorithm (MaxEnt 3.4.4), one of the most commonly used ENM algorithms was chosen due to its robust performance consistently demonstrated in numerous previous studies. 36,52,53 For both NA and CM ranges, we randomly generated 10 000 background points within each range. Then, we combined 70% of the thinned occurrence data and the data for seven environmental variables under historical climatic scenarios belonging to corresponding ranges to calibrate NA models and CM models, respectively, using the rest for model evaluation.…”