I examine population dynamics within pest-crop
agroecosystems including the potential effects of
climate change. Additionally, I create experimental
games to quantify the decision making processes in
various environmental arenas. To address these systems,
I synthesize information derived from field data,
experimental gaming data using a variety of quantitative
techniques including data simulation, population
modeling, spatiotemporal forecast modeling and landscape
ecology. An important goal of my work is the
creation of applicable and predictive models to reduce
the negative impacts on ecosystems. 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 a few ecological or social-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
3. Can we
use social-ecological theory to understand and nudge
human behavior to improve system resilience
I develop models that help inform Integrated
Pest Management systems, reduce the spread of disease in
our hoofstock industries, and improve water quality in
the Lake Champlain watershed. 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.
Past
Research Projects
Quantifying how climate change will influence
the phenology, habitat and seasonality of 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.
Validating 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.