Models describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology. Predictions of phenological events differ substantially between models that incorporate scale-appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate. Our work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale-appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development.

Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia Kurdjumov). Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors. Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models.

The Russian wheat aphid, Diuraphis noxia (Kurdjumov) is a serious pest of small grains, such as wheat and barley. High population growth rates and a broad gramineae host range have allowed this aphid to successfully establish and become pestiferous across much of North America since its invasion in the mid-1980s. Resistant wheat cultivars were developed and provided control of D. noxia until 2003, when a new biotype (designated RWA2, as contrasted with the original biotype designation, RWA1) emerged and rapidly spread through dryland winter wheat-growing regions. RWA2 displaced RWA1 more quickly than expected, based on RWA2 advantage in RWA1-resistant wheat cultivars. Previous research suggested that RWA2 may out-compete RWA1 in cooler temperatures. Thus, we sought to determine if RWA2 had a competitive advantage over RWA1 during the over- wintering period. We placed a known distribution of RWA1 and RWA2 aphids in the field for the winter at three sites across a latitudinal gradient (from northern Colorado to Texas) to test for a competitive advantage between these biotypes. We found overwhelming support for an overwintering competitive advantage by RWA2 over RWA1, with evidence suggesting a 10-fold advantage even at our Texas site (i.e., the site with the mildest winter). This substantial overwintering advantage helps explain the quick dispersion and displacement of RWA1 by RWA2.

The European corn borer, Ostrinia nubilalis (Hubner), is a damaging pest of numerous crops including corn, potato, and cotton. An understanding of the interaction between O. nubilalis and its spatial environment may aid in developing pest management strategy. Over a 2-yr period, 8,000 pheromone trap catches of O. nubilalis were recorded on pivot irrigated corn in northeastern Colorado. The highest weekly moth capture per pivot irrigated field occurred on the week of 15 July 1997 at 1,803 moths captured. The lowest peak moth capture per pivot-irrigated field was recorded on the week of 4 June 1998 at 220 moths captured. Average trap catch per field ranged from 1.6 moths captured per trap per week in 1997 to 0.3 moths captured per trap per week in 1998. Using pheromone trap moth capture data, we developed a quantified understanding of the spatial distribution of adult male moths. Our findings suggest strong correlations between moth density and adjacent corn crops, prevailing wind direction, and an edge effect. In addition, directional component effects suggest that more moths were attracted to the southwestern portion of the crop, which has the greatest insolation potential. In addition to the tested predictor variables, we found a strong spatial autocorrelation signal indicating positive aggregations of these moths and that males from both inside and outside of the field are being attracted to within-field pheromone traps, which has implications for refuge strategy management.

Wheat, the most important cereal crop in the Northern Hemisphere, is at risk for an approximate 10% reduction in worldwide production because of animal pests. The potential economic impact of cereal crop pests has resulted in substantial research efforts into the understanding of pest agroecosystems and development of pest management strategy. Management strategy is informed frequently by models that describe the population dynamics of important crop pests and because of the economic impact of these pests, many models have been developed. Yet, limited effort has ensued to compare and contrast models for their strategic applicability and quality. One of the most damaging pests of wheat in North America is the Russian wheat aphid, Diuraphis noxia (Kurdjumov). Eighteen D. noxia population dynamic models were developed from the literature to describe pest intensity. The strongest models quantified the negative effects of fall and spring precipitation on aphid intensity, and the positive effects associated with alternate food source availability. Population dynamic models were transformed into spatially explicit models and combined to form a spatially explicit, model- averaged result. Our findings were used to delineate pest intensity on winter wheat across much of the Great Plains and will help improve D. noxia management strategy.

Strategies for controlling pests are an integral part of any agricultural management plan. Most field crops, such as wheat (Triticum spp.) and corn (Zea mays L.) are managed as if they are homogeneous units. However, pests within fields are rarely homogenous. Development of plans that use targeted pest control tactics requires knowledge of the ecological drivers of the pest species. That is, by understanding the spatio-temporal factors influencing pest populations, we can develop a management strategy to prevent or reduce pest damage. This study was conducted to quantify variables influencing the spatial variability of adult male western bean cutworm, Striacosta albicosta (Smith). Striacosta albicosta moths were collected in pheromone traps in two center pivot, irrigated corn fields near Wiggins, CO. We hypothesized that moth abundance would be influenced by the distance from the edge of the field, distance to nearest alternative corn crop and affected by anisotropic effects, such as prevailing wind direction. Greater trap catches of S. albicosta in each of the fields were found with increased proximity to the edge of the field, if the nearest neighboring crop was corn. Prevailing wind direction and directional effects were found to influence abundance. Results serve as a first step toward building a precision pest management system for controlling S. albicosta.

The sunflower stem weevil, Cylindrocopturus adspersus (LeConte) (Coleoptera: Cur- culionidae), has caused yield losses across much of the western Great Plains. Little is known about the field biology of this pest. Simple prediction models, such as degree-day models, are integral tools for development of C. adspersus management strategies. Using data collected in Colorado, Kansas, and Nebraska, we sought for predictable variation between C. adspersus pupation, adult eclosion, and emergence and accumulated degree-days Celsius (DD) by using a temperature threshold of 5 degrees C. Accurate phenological models can be used to time scouting efforts and pesticide applications. The relationship between phenological data and accumulated DD nonlinear, Gaussian distributions better than uniform distributions. Phenological models were developed to describe these distributions for pupation, adult presence within the stalk and adult emergence. The pupation model predicts 50% pupation at 197 DD and 90% at 307 DD. Model results predict that 50% of adult eclosion within the stalks will have transpired at 396 DD and 90% at 529 DD. A model-averaged result from two data sets predicts 5% adult emergence from stalks at 262 DD, 50% emergence at 540 DD, 75% emergence at 657 DD, and 90% at 777 DD. Scouting for adults thus can be initiated at 262 DD. Current chemical controls target adults to prevent oviposition. Thus, applications therefore should not be made before this point.

The Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a pest of small grain crops that has caused hundreds of millions of dollars of damage since it was first reported in the United States in 1986. Much is known about D. noxia population dynamics during the spring and early summer when most of the crop damage occurs, whereas little is known about the system during the overwintering period. Using a spatially explicit model developed from field observations in a wheat/fallow agro ecosystem, we sought for predictable variation in overwintering success of D. noxia based on environmental factors such as topography and soil type. Successful modeling of densities of D. noxia would facilitate early control efforts targeting locations where D. noxia successfully overwintered. D. noxia density data were collected over 3 yr at two sites in eastern Colorado. The model incorporates georeferenced data from soil surveys, topography, and satellite imagery as predictor variables. Our approach links an information theoretic approach for model inference and model selection to landscape ecology, allowing for the examination of multiple candidate models and variables within each of the candidate models. Results were used to create trend surface models for D. noxia density in winter wheat agroecosystems. The model has the potential for use in site specific pesticide applications. Using site specific pesticide applications, pesticide inputs could be reduced by an estimated 30%, reducing input costs to the producer, increasing natural enemy refuges, reducing environmental contamination, augmenting pesticide resistance management practices, and reducing exposure of agricultural workers.

The Russian wheat aphid, Diuraphis noxia (Kurdjumov), is a significant pest of small grains in the United States and worldwide. There is an increasing need for quality population dynamic models to aid in development of integrated pest management strategies. Unfortunately, there exists high variability in published data regarding basic life history traits that frequently direct model parameterization. Metadata were analyzed to develop relationships between temperature and reproductive and developmental traits of D. noxia. Specifically, functions were developed between temperature and the following traits: lifespan, fecundity, fecundity rate, pre-nymphipositional period, reproductive period, and intrinsic rate of increase. Lower and upper temperature reproductive thresholds were calculated as 0.6 and 36.9 C, respectively. The lower temperature developmental threshold was calculated as -0.69 C. Modeled longevity reached its maximum at ~80 d. Meta-analysis indicates maximum fecundity at ~18.5 C, with a maximum fecundity rate of ~2.1 nymphs per day over the nymphipositional period. The calculated maximum total fecundity was ~55 nymphs per female. The maximum reproductive period was calculated to be 29.9 d. Compared with other aphid species, as temperature increased, the intrinsic rate of increase of D. noxia increased more slowly relative to Schizaphis graminum (Rondani) and Rhopalosiphum padi L., but at a similar rate to Sitobian avenae (F.).