Scott C. Merrill
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Research Overview


I explore questions involving dynamics of change within pest-crop agroecosystems, particularly at the landscape scale. Focused within this overarching research direction are interests in population modeling, landscape ecology, climate change, and spatiotemporal forecast modeling. I believe arthropod-crop agroecosystems are ideal study systems for examining many questions because of our ability to reduce system complexity. That is, agroecosystems have high accessibility and are highly managed, resulting in relatively uniform systems. For example, crop management typically specifies within-field cultivation using uniform seed density, plant age, and a single genetic crop variety. Additionally, soil conditions, weeds and insect populations are managed. Thus, from a reductionist point of view, system complexity is greatly reduced. Moreover, questions answered within agroecosystems frequently have management implications, providing an avenue for both scientific ingenuity and outreach. Therefore, my approach is to use theoretical modeling combined with field and laboratory experimentation to provide a framework for studying agroecosystems. My research has concentrated primarily on two general ecological questions:


1.      How will climate change influence the habitat, phenology and seasonality of plants and arthropods


2.      Can we quantify pest-crop agroecosystem dynamics using spatiotemporal models and rate functions for important arthropod species


Examining problems using a variety of different quantitative tools and comparing numerous models (multimodel inference) will stimulate scientific advances. I employ a varied methodology to aid in modeling efforts such as meta-analyses (Merrill et al. 2009a), bootstrapping (Kerzicnik et al. In prep), non-linear population modeling (Merrill et al. 2010), incorporating spatial dependencies including using spatial autocorrelated data (Merrill et al. Accepted, Merrill et al. Submitted-a), and combining Geographic information system (GIS) technologies with precision forecasting models (Merrill et al. 2009b, Merrill and Peairs Submitted, Merrill et al. Submitted-b). Other work includes examine pest life history traits to elucidate differences in plant-insect interactions (Randolph et al. 2008, Merrill et al. 2010) and survey work throughout Colorado to help understand the spread of newly discovered virulent biotypes of the Russian wheat aphid (Merrill et al. 2008). While I have enjoyed applying my modeling skills in arthropod-crop agroecosystems, I am enthusiastic collaboration that incorporates my interests in spatial and quantitative population modeling.

Current Research Projects

Quantifying how climate change will influence the phenology, habitat and seasonality Corn harvest for yields 2006of important arthropod pests and their host plants
How will climate change affect our food security? Although many of the effects of climate change on agriculture have been postulated, spatiotemporal prediction models have not been forthcoming. Agricultural management requires a well-informed strategy for controlling pests. Prediction models that quantify arthropod pest dynamics are key components for developing agricultural management strategy and, because of their ectothermic nature, the arthropod population dynamics are is tightly linked to climatic variables. Thus, prediction models frequently use climate variables as drivers. Currently, I am developing spatiotemporal models to describe how existing climate data can be extended to quantify the effects of climate change on pest dynamics. This method explicitly incorporates daily temperature variation, without which substantial error propagation in phenological modeling will occur. To illustrate this method, it was applied to examine the phenology of the Sunflower stem weevil (Merrill and Peairs In Prep), which produced an unexpected but exciting result. Specifically, climate change simulations changed the duration of the sunflower crop’s at-risk window for weevil oviposition, with some areas observing a shorter at-risk time window while others experience a longer at-risk window. This counter-intuitive result serves to emphasize the need for increased research on the effects of climate change on important ecosystems. Knowledge such as produced by Sunflower stem weevil modeling can help crop managers or ecosystem stakeholders predict and respond to climate change.

Ongoing modeling efforts will depict likely shifts in habitat quality for the Russian wheat aphid by incorporating knowledge of aphid biology and alternate plant hosts. For example, the Russian wheat aphid feeds exclusively on plants utilizing the C3 photosynthetic pathway. Can we predict likely changes to the habitat quality of the Russian wheat aphid by examining postulated climate change effects on the habitat of C3 grass species (Collatz et al. 1998)? Moreover, a warmer climate may increase the aphid’s daily intrinsic rate of increase. Will changes to our climate result in higher aphid incidence, and more economically damaging infestations? 

Russian wheat aphid outbreak prediction models developed for use throughout the Great Plains states
Quantitative knowledge of pest population dynamics and eco-physiological factors are essential for the development and implementation of quality integrated pest management. One of the principle pests of wheat across the Great Plains is the Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov). This aphid pest has caused damage in excess of a billion dollars in the last two decades. We intend to use a database developed over four years, across five states with approximately 70,000 data points to develop an increased understanding of RWA ecology. Specific objectives are as follows: 1) Investigate seasonal dynamics of RWA under the influence of weather variables. 2) Model suitable habitat of RWA using agro-climatic conditions. 3) Quantify the effects of aphid natural enemies on RWA populations. 4) Develop action thresholds for RWA. And 5) develop a spatiotemporal model of RWA population dynamics in wheat. We intend to address all three program priorities. Specifically, we propose to 1) determine eco-physiological mechanisms that affect abundance of RWA; 2) characterize population ecological processes that affect establishment (models detailing likely habitat and spatiotemporal abundance) of RWA; and 3) elucidate multitrophic interactions between RWA, beneficial organisms and winter wheat. Resulting quantitative models will be applicable to predicting pest outbreaks as well as developing risk scenarios.

Plot in lamar field site
                    covered in snowValidating a spatially-explicit precision forecasting model for Russian wheat aphid densities on small grain crops in Colorado
Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov) is a pest of wheat and barley. Damage estimates are in the hundreds of millions of dollars since its introduction into the United States. We have built a predictive model with the goal of explaining within-field variation in RWA population structure using weather variables, soil characteristics, topography and Landsat 7 Enhanced Thematic Mapper imagery. This model has the potential to be a key Integrated Pest Management tool (e.g. forecasting, placement of resistant small-grain varieties, precision pesticide application and directed scouting). Cross-validation suggests that this model will predict RWA densities during the early spring on winter wheat as or more precisely than conventional field scouting, relying entirely on remotely sensed data. However, the model has not been validated for other cereals (e.g., barley), or time periods (e.g., late spring or early summer). We propose to validate the model under field conditions for high-resolution forecasting of RWA densities using winter wheat and spring barley, from the early spring to harvest. Additionally, we intend to correlate RWA population densities with yield damages. Combining loss functions, temperature variables, and the spatially explicit RWA density model will generate RWA Risk Assessment Maps, which will serve to focus control efforts in areas of greatest need.

Spatial variability of Western bean cutworm (Lepidoptera: Noctuidae) pheromone trap captures in sprinkler irrigated corn in eastern Colorado
While most crops are still managed as if they were homogeneous units, this strategy ignores within-field heterogeneous characteristics that could be used to reduce costs. Precision agriculture utilizes heterogeneity in the landscape (e.g., differing soil characteristics) to target tactics such as variable seeding and fertilization rates. An under-utilized aspect of precision agriculture is precision pest management, which targets control tactics to within-crop sites to reduce economic pest damage. Determining which variables influence pest distributions is a key element in the development of precision pest management strategy. That is, by understanding the factors influencing a pest’s spatial distribution, we may be able to target management efforts to spatially-explicit zones or sites to prevent or reduce pest damages. This study was conducted to generate an understanding of spatial variability of Western bean cutworm, Striacosta albicosta (Smith). Using augmented, grid-based sampling, S. albicosta moths were collected in pheromone traps at 371 locations in 1997 and 359 locations in 1998 in two center pivot-irrigated corn fields near Wiggins, Colorado. We hypothesized that distance from the edge of the field and distance to nearest alternative corn crop would influence moth abundance. Anisotropic effects, such as prevailing wind direction, were tested to determine if directional patterns existed in addition to the aforementioned covariates. Greater trap catches of S. albicosta in each of the fields were found with increased proximity to the edge of the field. Trap catches were greater if the nearest neighboring crop was also corn. Prevailing wind direction and anisotrophic effects were found to influence abundance.

Spatial variability of European corn rootworm pheromone trap captures in sprinkler irrigated corn in eastern Colorado
Field corn, Zea mays L., is one of the most economically important crops throughout the United States with production increasing to over 13 billion bushels in 2009 and garnering over $47 billion dollars in 2008. Unfortunately, this crop is at risk for substantial economic losses from pests. To limit pest damages and increase yields complex management systems are being used. One management system is currently under utilized is precision agriculture. Precision agriculture, in contrast to traditional management systems, seeks to use known heterogeneity in the cropping system to maximize profit (e.g., using field heterogeneity to maximizing yield). Precision pest management is a sub discipline of Precision Agriculture that seeks to use field heterogeneity to target pest controls. To target pest controls, accurate pest information is needed. Models and maps that predict or depict areas of the field at risk for economic pest damage are an important component of the precision pest management system. We seek to develop predictive models for major pests of corn that direct management decisions and deployment of controls. One of the more damaging pests in corn is the European corn borer, Ostrinia nubilalis (Hübner). Current efforts are underway to develop a spatiotemporal model for O. nubilalis moth infestations in corn. We are examining the effects of variables include prevailing wind, distance from the edge of the corn field, and anisotropic variables (possibly insolation driven).

Referenced Above

Collatz, G. J., J. A. Berry, and J. S. Clark. 1998. Effects of climate and atmospheric CO2 partial pressure on the global distribution of C-4 grasses: present, past, and future. Oecologia 114: 441-454.

Kerzicnik, L. M., F. B. Peairs, J. D. Harwood, and S. C. Merrill. In prep. Spiders in diverse cropping systems.

Merrill, S. C., and F. B. Peairs. Submitted. Climate change will influence the timing of pest attacks. Nature Climate Change

Merrill, S. C., T. O. Holtzer, and F. B. Peairs. 2009a. Diuraphis noxia reproduction and development with a comparison of intrinsic rates of increase to other important small grain aphids: a meta-analysis. Environmental Entomology 38: 1061-1068.

Merrill, S. C., T. O. Holtzer, and F. B. Peairs. Accepted. Examining Spatial Correlation Between Fall and Spring Population Densities of the Russian Wheat Aphid (Hemiptera: Aphididae). Colorado State University Agricultural Experiment Station Technical Report.

Merrill, S. C., T. L. Randolph, C. B. Walker, and F. B. Peairs. 2008. 2007 Russian wheat aphid biotype survey results for Colorado, pp. 43-44. In J. J. Johnson [ed.], Making better decisions: 2007 Colorado wheat variety performance trials. Colorado State Univ. Agric. Exp. Sta. Tech. Rep. TR08-08. Colorado State University, Fort Collins, CO.

Merrill, S. C., T. O. Holtzer, F. B. Peairs, and P. J. Lester. 2009b. Modeling spatial variation of Russian wheat aphid overwintering population densities in Colorado winter wheat. Journal of Economic Entomology 102: 533-541.

Merrill, S. C., A. Gebre-Amlak, J. S. Armstrong, and F. B. Peairs. 2010. Nonlinear degree-day models of the Sunflower stem weevil (Curculionidae: Coleoptera) Journal of Economic Entomology 103: 303-307.

Merrill, S. C., S. M. Walter, F. B. Peairs, and J. A. Hoeting. Submitted-a. Spatial Variability of Western Bean Cutworm Populations in Irrigated Corn. Environmental Entomology.

Merrill, S. C., T. O. Holtzer, F. B. Peairs, and P. J. Lester. Submitted-b. Prediction of Spatially Explicit Russian Wheat Aphid Densities in Winter Wheat Agroecosystems. Journal of Economic Entomology.

Randolph, T. L., S. C. Merrill, and F. B. Peairs. 2008. Reproductive rates of Russian wheat aphid (Hemiptera : Aphididae) biotypes 1 and 2 on a susceptible and a resistant wheat at three temperature regimes. Journal of Economic Entomology 101: 955-958.

Some of my favorite pictures