Empirical and mechanistic models predict pinyon ips (Ips confusus) phenology

Abstract

Pinyon ips (Ips confusus LeConte) is a native bark beetle that can cause extensive mortality of pinyon pines (Pinus spp.) in the western United States, particularly during hot droughts that stress trees. Although pinyon ips is assumed to be multivoltine, with multiple generations per year, research is lacking regarding temperature-dependent phenology and field-based evidence of annual generations. At sites in Arizona and New Mexico, across two years, we monitored pinyon ips lifecycle timing using emergence cages on trees and passive traps. These data and associated field-based temperatures were used to develop empirical models that predict timing of spring emergence and summer generations. For comparison to field-based models, laboratory experiments were conducted to quantify pinyon ips lifestage-specific development at constant temperatures and a cohort-based mechanistic phenology model was developed. Field measures highlight extensive overlap of summer generations, likely due to extended oviposition, variable microhabitat temperatures, and developmental variability among lifestages. Predictions from the two models were in alignment with each other and with field-based data for spring emergence of an overwinter generation, and the first and second summer generations. Both models also predicted the observed number of summer generations, although cohort splitting overwinter was difficult to quantify. The warmest sites were predicted to have up to 5 annual generations with 2 generations at cool sites. Increasing severity of hot droughts will likely increase pinyon ips caused tree mortality as the number of annual generations increases. The models can be used to estimate pinyon ips generations given daily temperature records.

Citation

Hansen, E. M.; Powell, J. A.; Graves, A.; Souder, S.; Vandygriff, J. C.; Gaylord, M.; McMillin, J.; Bentz, B. J. 2025. Empirical and mechanistic models predict pinyon ips (Ips confusus) phenology. Ecological Modelling. 510: 111282.