Seasonal Occurrence and Development of Degree-Day Models for Predicting Activity of Conotrachelus nenuphar (Coleoptera: Curculionidae) in Alabama Peaches

Abstract

The plum curculio, Conotrachelus nenuphar (Herbst) (Coleoptera: Curculionidae), is a key pest of peaches, Prunus persica (L.) Batsch, in North America. Captures of adult weevils in unbaited pyramid traps recorded annually from 2000 to 2008 in an unmanaged peach orchard in central Alabama were used to determine its seasonal occurrence and to develop predictive degree-day models. Spring migration of plum curculio began at bloom (early to mid-March). Linear, polynomial, and three-parameter Weibull functions were tested to describe the relationship between weekly trap capture and cumulative degree-day (DD). Criteria used to select the best models were the smallest Akaike information criterion and highest R2 values. A sixth-order polynomial function fitted best to seasonal trap captures and cumulative DDs and revealed two major seasonal peaks with the first (spring generation) and second (summer generation) peaks occurring at cumulative DDs of ≈245 and 1105 (base 10°C, biofix of 1 January), respectively. A potential third (late summer generation) peak was observed at 1758 DDs. The sixth-order polynomial model predicted the first trap capture to occur at cumulative DD of ≈99 (base 10°C, biofix of 1 January). The three-parameter Weibull model predicted the first trap and first peak (spring generation) trap captures to occur at mean cumulative DDs of 108.02 ± 9 and 220.07 ± 16, respectively. Validation of the models in the unmanaged orchard in 2009 and 2010 and in a second unmanaged orchard (located 1.6 km from the first) in 2009 showed that the polynomial and Weibull were within ±7 d in their predictions of the first and peak trap captures of the spring population. Validation results showed that both models successfully predicted the first trap capture in one out of three scenarios and the peak trap capture in two out of three scenarios. The performance of the models is discussed in relation to management of plum curculio in central Alabama.

Evaluation of Phenological Indicators for Optimizing Spring Southern Pine Beetle (Coleoptera: Curculionidae: Scolytinae) Trapping Surveys

“…The peak D. frontalis and T. dubius spring dispersal period was defined as the continuous 4 wk period that the observed trap captures were greater than any other continuous 4 wk period. Weekly totals also were converted to a percentage of the 5 mo total spring trap captures to evaluate the yearly variation in population size (Akotsen-Mensah et al 2011). The percentage of all beetles collected during the peak period was calculated.…”

“…However, effective control requires nursery managers to be able to predict R. frustrana egg hatch and larval development in order to time the application of insecticides to coincide with these life history events (Douce et al 2002). The aforementioned examples express why researchers have developed predictive models (Gargiullo et al 1985;Kumral et al 2007;Knutson & Muegge 2010;Akotsen-Mensah et al 2011;Haavik et al 2013) or observational cues (Mussey & Potter 1997;Herms 2004;Reding et al 2013;Hartshorn et al 2016 ) to predict key life history events for many serious insect pests.…”

Pest categorisation of Conotrachelus nenuphar

“…Akotsen‐Mensah et al. () developed a day‐degree phenology model linking accumulated temperature with weekly trap captures in Alabama peach orchards. The spring generation of adults peaked at 245 DD above 10°C after 1 January; the summer generation of adults peaked at 1,105 DD and a late summer generation peaked at 1,758 DD.…”

Using degree-day and nonlinear regression models to predict seasonal flights of Adoxophyes orana (F.V.R.) (Lepidoptera: Tortricidae) in plum orchards

“…Degree-day models are considered as a useful tool for forecasting the seasonal flight activity of many lepidopterous pests (Hrdý et al, 1996;Del Tio et al, 2001). These models have been used more effectively for making pest control decisions (Akotsen-Mensah et al, 2011). For example, models based on linear and nonlinear functions may help predict insect phenology such as emergence time of the codling moth Cydia pomonella (L., 1758) in the field (Demir & Kovancı, 2015).…”