Modeling the abundance of two Rhagoletis fly (Diptera: Tephritidae) pests in Washington State, U.S.A.
Modeling the abundance of two Rhagoletis fly (Diptera: Tephritidae) pests in Washington State, U.S.A.
Editor: Frank H. Koch
USDA Forest Service
Tewodros T. WakieID 0
Wee L. Yee 0
Lisa G. NevenID 0
0 USDA-ARS, Temperate Tree Fruit and Vegetable Research Unit, Wapato, Washington, United States of America , 2 Natural Resource Ecology Laboratory , Colorado State University , Fort Collins, Colorado , United States of America
Well-adapted and abundant insect pests can negatively affect agricultural production. We modeled the abundance of two Rhagoletis fly (Diptera: Tephritidae) pests, apple maggot fly, Rhagoletis pomonella (Walsh), and western cherry fruit fly, Rhagoletis indifferens Curran, in Washington State (WA), U.S.A. using biologically relevant environmental variables. We tested the hypothesis that abundance of the two species is influenced by different environmental variables, based on the fact that these two species evolved in different environments, have different host plants, and that R. pomonella is an introduced pest in WA while R. indifferens is native. We collected data on fly and host plant abundance at 61 randomly selected sites across WA in 2015 and 2016. We obtained land-cover, climate, and elevation data from online sources and used these data to derive relevant landscape variables and modeled fly abundance using generalized linear models. For R. pomonella, relatively high winter mean minimum temperature, low elevation, and developed land-cover were the top variables positively related to fly abundance. In contrast, for R. indifferens, the top variables related to greater fly abundance were high Hargreaves climatic moisture and annual heatmoisture deficits (indication of drier habitats), high host plant abundance, and developed land-cover. Our results identify key environmental variables driving Rhagoletis fly abundance in WA and can be used for understanding adaptation of insects to non-native and native habitats and for assisting fly quarantine and management decisions.
Data Availability Statement: The climate data
used in this study were downloaded from the
following website: https://sites.ualberta.ca/~
ahamann/data.html. The authors did not have any
special privileges other interested researchers
would not have. The National Land-cover data used
in this study were downloaded from the following
website: https://www.mrlc.gov/data ("National Land
Cover Database 2011 (NLCD2011)"). The authors
did not have any special privileges other interested
researchers would not have.
The threat of invasive insects including those that threaten agriculture is a function of how
well they adapt to their non-native habitats and ultimately their abundance in or near
]. Pest species that evolved in other environments and then invade non-native
regions may only develop high abundance in those localities within these regions most similar
to their native habitats. These localities may differ from those occupied by native pest species
that have evolved under different environmental pressures, which may be explained by
Funding: L.N., W.Y., and S.K. received funding
from the Washington Tree Fruit Research
agreement number 58-5352-1-301, through a
grant originating from the USDA, Foreign
Agriculture Service, Technical Assistance for
Specialty Crops program. The funders had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
adaptation to different host plant use in the case of frugivorous insects that ultimately is related
to climate. From a practical standpoint, habitats where the climate and other environmental
factors are conducive for favorable insect survival and thus high population build-up are areas
where agricultural commodities will be most threatened. Differences among pest species in
their ability to tolerate particular climates and habitats will result in different containment or
management approaches for the pests.
Habitat heterogeneity and complexity as governed by vegetation type, structure,
topography, and host abundance may contribute to abundance of some insect species [
that can use a wide range of host plants or can tolerate a wide range of temperatures would be
expected to have a greater distribution than those more limited in either ability. While many
studies have focused on habitat heterogeneity and animal species diversity (more complex
environments have more species), few have explored the relationship between habitat
heterogeneity and abundance within insect species [
In the Pacific Northwest of the U.S.A., the apple maggot fly, Rhagoletis pomonella (Walsh)
(Diptera: Tephritidae), is a major quarantine pest of commercial apple (Malus domestica
Borkhausen), which is the top fruit commodity in the region. In Washington State (WA) alone,
apple production in 2016 was valued at U.S. $2.4 billion . Evidence shows R. pomonella is
not native to the Pacific Northwest but was probably introduced there in infested apples from
eastern North America prior to the 1970s [10,11]. In WA, R. pomonella attacks unmanaged
cultivated apples, native black hawthorn (Crataegus douglasii Lindley) and introduced
ornamental hawthorns (Crataegus monogyna Jacq. and Crataegus laevigata (Poir. DC.) [12, 13]).
The abundance of R. pomonella in these host plants within WA influences their importance
and threat to the apple industry, which is concentrated in the dry but irrigated central interior
of the state.
Western cherry fruit fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), is a major
quarantine pest of commercial sweet cherry (Prunus avium (L.) L.) in the Pacific Northwest.
Washington is the top sweet cherry-producing state, as sweet cherries from there were valued
at U.S. $500 million in 2016 . Rhagoletis indifferens was originally found in the foothills of
the Cascade Mountain Range around the margins of the commercial cherry-growing region in
central WA in its native host, bitter cherry (Prunus emarginata [Dougl. ex Hook.] Eaton) .
However, R. indifferens moved onto cultivated sweet cherry and tart cherry (Prunus cerasus L.)
after they were introduced into the region in the mid-1800s [14,15]. The fly was first detected
in a cherry-growing region in central WA in 1942 in Toppenish in Yakima County .
Rhagoletis indifferens is also found over a broad area extending to western WA , indicating it is
capable of surviving in a wide range of climates and habitats in WA.
Previous work suggests that the most suitable habitats for R. pomonella and R. indifferens
are not the same due to differences in the flies? environmental niches [17, 18]. Within WA,
there is substantial heterogeneity and diversity of habitats associated with variable climates,
ranging from coastal forests and subalpine ecosystems west of the Cascade Mountain Range to
ponderosa pine, bunchgrass, sagebrush, and interior cedar-hemlock ecosystems to the east
. Abundance patterns of R. pomonella and R. indifferens across these diverse habitats may
differ. Differential tolerance to climatic factors such as temperature, precipitation, and
humidity could influence the abundance of R. pomonella and R. indifferens across these habitats.
Rhagoletis pomonella appears less able to survive dry climates than R. indifferens [20?23]. This is
consistent with the hypothesis that R. pomonella survives best in climates resembling those
where it evolved in eastern North America, where relative humidity is higher than in many
areas of the western U.S.A. Conversely, R. indifferens evolved on bitter cherry in drier
environments, such as in the aforementioned foothills of the Cascade Mountain Range, and thus may
survive better in lower humidity and precipitation environments.
2 / 17
Rhagoletis pomonella is dependent mostly on Malus and Crataegus species to survive, so
another logical hypothesis is that abundance of these hosts is the most important factor
explaining fly abundance. However, there are instances where host tree abundance does not
seem to explain fly abundance. Rhagoletis pomonella is rare in central WA, even where native
hawthorn trees are abundant . This suggests environmental factors other than host plant
abundance could explain fly abundance in an area.
Host abundance should also explain the abundance of R. indifferens. However, this fly
appears less abundant in western than central WA [12, 24] even though there are many
unmanaged cultivated cherry and native bitter cherry trees in western WA . Thus, there
could be similarities and differences in the environmental factors affecting the abundance and
distribution of R. pomonella and R. indifferens.
The objective of this study was to examine the role of climate and landscape heterogeneity
on the abundance of R. pomonella and R. indifferens in WA. We tested the hypothesis that
abundance of the two species is influenced by different environmental variables, specifically
that moisture, cold temperature, host plant abundance and landscape heterogeneity could
limit the abundance of R. pomonella while humidity, precipitation, host plant abundance, and
landscape heterogeneity could limit the abundance of R. indifferens. We predict differences in
fly abundance across eastern, western and central regions of WA and discuss implications of
the results for adaptation of flies to non-native and native habitats and for fly quarantines and
Materials and methods
Fly surveys were conducted in 2015 and 2016 at 61 sites in WA using a Geographic
Information System (GIS)-based stratified random sampling approach. Random samples were
generated for the state of WA using the Hawth?s analysis tools for ArcGIS software by selecting the
generate random points option . We selected this sampling approach because the technique
was found to be more efficient when compared with other statistical sampling methods .
Initially, more than 100 random points were generated in a GIS environment using elevation,
temperature, and precipitation data layers as stratifying units (the aim was to collect
environmentally heterogeneous samples). However, some of the randomly generated points were
located in inaccessible sites such as a side of a cliff or a top of a mountain, thus only 61 sites in
total were surveyed (Fig 1). In 2016, host plant species presence and abundance data were
collected at each site by constructing 0.1-ha circular plots. Host trees used for trapping were
considered the centers of the circular plots, and all host plants within the circular plot (seedlings,
saplings, and mature) were counted. We represented host abundance by the total number of
plants found in the circular plots, and abundance of flies by the average number of R.
pomonella and R. indifferens adult flies trapped at each site throughout the study period. Flies were
captured by placing a single trap per site per species.
Fly abundance data were collected from the same 61 sites using sticky yellow rectangle traps
baited with ammonia odor in May to October 2015 and 2016. The Pherocon AM trap (Trece?,
Adair, OK, U.S.A.) baited with the P203 AB27 lure (AgBio Inc., Westminster, CO, U.S.A.) was
used for trapping R. pomonella and R. indifferens. The lure contains 27 g of ammonium
bicarbonate and at 30?C has a release rate of 12.8 mg ammonium bicarbonate/h . Traps were
hung on host trees 1.6 m above ground and replaced every two weeks. Sites were visited 12?
14 times during the trapping period. Trapping ended when the first hard freeze was recorded
based on freeze indicator detectors (American Thermal Instruments, Dayton, OH, U.S.A.).
Recovered traps were stored at -20?C until they could be processed. The sticky adhesive was
dissolved from the traps using citrus solvent (De-Solv-It orange solution, Household products,
3 / 17
Fig 1. Survey sites, and major apple and cherry growing counties. Fly and host plant abundance data were collected from 61 randomly generated sites
(shown in black dots) in 2015 and 2016. Major commercial apple growing regions in WA include all the major commercial cherry growing counties, and
Kittitas, Adams, Klickitat and Walla Walla Counties. Major commercial cherry growing counties in WA are Yakima (28%), Chelan (18%), Benton (15%),
Grant (12%), Douglas (10%), Franklin (~9%), and Okanagan (~9%). Data source: WA Apple Commission.
Gilbert, AZ, U.S.A.) before flies were removed. Flies were identified to the species level and
counted at the USDA-ARS Temperate Tree Fruit and Vegetable Research Unit in Wapato,
WA. Pest abundance data for each species were summed and averaged over the two study
years before statistical analyses were performed.
Environmental variables were obtained from different sources. The 100 m
spatial-resolution elevation data set for the study site was obtained from the U.S. Geological Survey website
. Using this elevation data set, ArcGIS software , and the surface gradient and
geomorphometric modeling toolbox , six topographic variables that represent the spatial
heterogeneity of each study site were calculated. These metrics included compound topographic index,
site exposure index, surface roughness, flow direction, slope, and aspect. Furthermore, we used
the U.S. National Land-Cover Database to represent the different land-cover classes found at
the study sites .
The dispersal distances of both R. pomonella and R. indifferens adult flies are relatively
short. Previous studies indicate most R. pomonella flies are found within ~60 m of release
locations , while most R. indifferens flies are recovered within ~36 m of release locations .
Given the relatively short dispersal distances of the two flies, we represented landscape
heterogeneity using land-cover proportions close to the trap sites. Proportions were calculated in an
area having a circular radius of 120 m (2 X 60 m), and with the trap trees at the center. The
randomly generated 61 trap sites fell on seven land-cover categories, including developed
(open space, low-intensity, medium-intensity, high-intensity), cultivated crop, evergreen
forest, grassland-herbaceous, herbaceous-wetland, pasture-hay, and shrub-scrub categories (S1
Appendix). However, when we considered the 120 m-radius circular area, additional
land4 / 17
cover categories were identified. These included open water, barren-land, woody wetlands,
mixed forest, and deciduous forest categories (S1 Appendix). We aggregated closely related
land-cover categories before statistical analyses. All developed land-cover categories were
reclassified as developed, all forest classes were grouped into forest, and all wetland classes
were reclassified as wetland.
The climatic conditions of the study sites were represented using climate variables obtained
from the ClimateWNA program [34, 35]. ClimateWNA uses the Parameter-Elevation
Relationships on Independent Slopes Model (PRISM) baseline information [
] to generate
highresolution climate data for western North America. The program can be used to generate
scale-free climate data for any location by specifying latitude, longitude, and elevation. Here,
normal, annual, and seasonal variables for the most current time period, 1981?2010, were
used. Using geographically referenced trap site locations and the elevation data set, more than
100 biologically relevant climate variables were extracted from the ClimateWNA program.
These included 23 annual and 60 seasonal climate variables (S2 Appendix). The ClimateWNA
data were effectively used to predict the geographic distribution of invasive cheatgrass (Bromus
tectorum L.) in Colorado, U.S.A. , and the mountain pine beetle (Dendroctonus ponderosae
[Hopkins]) in western North America [
Relationships with host availability, climate variability, and landscape heterogeneity have
been modeled using different approaches including linear models and their extensions [
]. In this study, R. pomonella and R. indifferens abundance at the 61 locations were
modeled using host abundance data, landscape metrics, and topographic and climate variables.
We used linear models for preliminary analysis and investigated the magnitude and
direction of relationships among the response and predictor variables (each variable was tested
separately). Abundance data were log-transformed (log10+1) prior to linear modeling. This
preliminary analysis helped us identify the important variables and reduced the variables
from more than one-hundred to just 20. In addition, cross-correlation tests conducted in R
statistical software [
] indicated most of the ClimateWNA variables to be highly correlated
(Tables 1 and 2). Here we explained the observed abundance of R. pomonella and R.
indifferens, choosing one representative variable out of each set of highly correlated variables
(Pearson?s correlation r ? 0.7) from the models. Despite a slightly higher correlation with the
climate variables, we kept elevation in one of the models (the R. pomonella model), because
elevation improved model results when either used separately or in combination with other
For the final analyses, generalized linear models (GLM) were selected based on the
assumption that our non-transformed fly count data have a Poisson distribution. GLM fits
lesscomplex models, and its results are relatively easy to interpret. The statistical analyses were
conducted in R statistical software environment using the glm function and R packages
including stats, MASS, and Hmisc [
]. We ran several GLM models using different combinations of
variables and selected the best model using lowest Akaike?s Information Criterion (AIC)
values. AIC, which penalizes complex models, allows the selection of the most parsimonious
model among a set of different models [
To test for differences in R. pomonella and R. indifferens abundance across western, central,
and eastern WA, the mean numbers of flies caught on traps per county in each region were
calculated. The numbers of counties included in the study within each region were replicates.
Western WA comprised seven counties, central WA four counties, and eastern WA eight
counties (western: King, Whatcom, Cowlitz, Snohomish, Clallam, Gray?s Harbor, and Kitsap;
central: Kittitas, Yakima, Chelan, and Okanogan; eastern: Lincoln, Walla Walla, Garfield,
Asotin, Stevens, Ferry, Spokane, and Pend Oreille). Within each county, there were one to nine
sites (= traps). Means for each county were calculated based on data from the two seasons.
5 / 17
Because the data did not meet the assumption of normality, a Kruskal-Wallis test was
conducted on rankings of abundance within each species across regions.
The most important variables for explaining R. pomonella abundance in the final GLM
model included winter mean minimum temperature (Tmin_wt), elevation, and four
different land-cover types (Table 3). Host plant abundance was an important variable that was
positively associated with R. pomonella abundance, but it was not as important as winter
temperature. Furthermore, the AIC of the GLM model increased when host plant was
included in the model. All land-cover categories included in the GLM model were positively
associated with R. pomonella abundance. The developed, cultivated crops, and pasture-hay
categories were highly significant while the shrub-scrub category was moderately significant
In the linear models, elevation was negatively associated with fly abundance, as were low
mean minimum temperatures, low winter degree-days below 5?C, and low numbers of
frost6 / 17
free days (S1 Fig). Other than elevation, the topography or landscape metrics considered in
this study did not have a significant role in explaining the distribution of R. pomonella. Based
on percent land-cover (Fig 2a and 2b), R. pomonella has relatively high abundance in
developed areas (consistent with Table 3); relatively low abundance in the cultivated crops and
forests; very low abundance in pasture-hay and shrub-scrub categories; and zero or near zero
abundance in the other land-cover categories.
Our results indicate that western WA counties had greater R. pomonella abundance than
central and eastern WA counties (Fig 3). Statistical tests indicated that the number of R.
pomonella caught on traps in western WA was significantly greater than in central and eastern WA,
based on mean ranks of counts (14.5, 7.5, and 7.3, respectively: ?2 = 7.66; df = 2; P = 0.0217).
The mean numbers of R. pomonella caught ? SE in western, central, and eastern WA were
12.6 ? 9.0, 0.1 ? 0.1, and 0.6 ? 0.5, respectively. There was no difference in numbers of flies
caught in central and eastern WA.
PLOS ONE | https://doi.org/10.1371/journal.pone.0217071
7 / 17
The most important variables for explaining R. indifferens abundance in the GLM model
included winter Hargreaves climatic moisture deficit (CMD_wt), annual heat-moisture index
(AHM), host plant abundance, and three different land-cover types (Table 4). Among the
land-cover categories, developed areas, shrub-scrub, and cultivated crops were positively
associated with R. indifferens abundance and were highly significant. The grassland-herbaceous
and wetland categories were both significant but negatively associated with R. indifferens
abundance (Table 4).
In the linear models, unlike for R. pomonella, as the moisture deficit increased (areas
become drier), the abundance of R. indifferens also increased (S2 Fig). Specifically, annual and
summer Hargreaves climatic moisture deficit (CMD_sm and CMD), summer heat-moisture
index (SHM_sm), and Hargreaves reference evaporation (Eref), were positively associated
with R. indifferens abundance (S2 Fig). Also unlike for R. pomonella, elevation did not play a
significant role in explaining the distribution of R. indifferens in this study. Based on percent
land-cover (Fig 2), the abundance of R. indifferens was highest in developed areas; medium in
Fig 2. Abundance of R. pomonella and R. indifferens in Washington State, U.S.A. (A) = total abundance by land-cover, and (B) = sample frequency by land-cover.
C.C = cultivated crops; D = developed areas; F = forest; G.H. = grassland-herbaceous; P.H. = pasture-hay; W = wetland; S.S. = shrub-scrub.
8 / 17
Fig 3. Abundance of R. pomonella in Washington State, U.S.A. Abundance data collected at 61 sites in 2015 and 2016 were averaged and classified into 5 classes
using the natural breaks classifier in ArcGIS [
cultivated crops and forest; low in the wetlands and shrub-scrub areas; and zero in the other
In contrast to R. pomonella (Fig 3), R. indifferens abundance was greater in central than
western WA (Fig 4). The number of R. indifferens caught in central WA was numerically
9 / 17
Fig 4. Abundance of R. indifferens in Washington State, U.S.A. Abundance data collected from 61 sites in 2015 and 2016 were averaged and classified into 5 classes
using the natural breaks classifier in ArcGIS [
greater than in western and eastern WA, although differences were not significant based on
mean ranks of counts (14.4, 9.1, and 8.6, respectively; ?2 = 3.13; df = 2; P = 0.2088). The mean
numbers of R. indifferens caught ? SE in central, western, and eastern WA were 50.4 ? 21.6,
7.1 ? 3.9, and 17.5 ? 12.6, respectively.
The results of our study support the hypothesis that abundance of R. pomonella and R.
indifferens is influenced by different environmental variables. Results show how two members within
a genus of frugivorous insects can have different habitat requirements to maximize population
growth, as measured by abundance, even though they are found in broadly overlapping
habitats. This may not be surprising given the relatively distant phylogenetic positions of the two
species within Rhagoletis [
] and their evolution on host plants with different fruiting
phenologies, which probably influences their ability to survive in different habitats. Ultimately as
shown in this study, R. pomonella is more abundant in the relatively wet region of western WA
than drier central WA while the reverse is true of R. indifferens.
Drivers of R. pomonella abundance
Our analyses show that R. pomonella in WA is most abundant and therefore survives optimally
in areas with warmer winters. These areas are the lower-lying regions west of the Cascade
Mountain Range. The lower abundance and apparent poorer survival of R. pomonella in
colder regions may be related to where the fly evolved in eastern North America. Winter
10 / 17
temperatures where R. pomonella evolved in eastern North America are generally warmer than
those at higher elevations in WA [
]. However, the presence of flies in colder sites such as
Ellensburg in the current study suggests there may be subpopulations of flies in eastern North
America (the presumed original source of WA flies) that are adapted to low temperatures.
Elevation was the most important landscape metric related to R. pomonella abundance,
probably because higher elevation is directly related to lower temperatures. Ultimately, R.
pomonella may have poorer survival in the colder, drier upland forests or foothills of the
Cascade Mountains than in the warmer, lower elevations west of the Cascades. Fly abundance in
relation to three measures of temperature (S1 Fig) are all consistent with cold adversely
affecting R. pomonella survival. In addition, areas that are cold but where there is no snow cover in
some years due to a drier environment can lead to lower soil temperatures in the winter [
further reducing R. pomonella survival.
Host plant abundance was an important variable explaining R. pomonella abundance in the
GLM and linear models, but it was not among the top variables in either model. Thus, results
suggest that high abundance of hawthorn, apple, or both hosts in an area will not necessarily
result in a high abundance of flies if the climate in that area is too cold. This implies that
climate or habitat suitability differences can exist between an invasive insect such as R. pomonella
and its hosts native to the area. This is important for predicting the potential abundance of a
non-native insect even when suitable, or marginally suitable, hosts are present. This
interpretation is consistent with data showing low or no R. pomonella infestations in hawthorn or apple
fruit in WA and Montana even where hawthorn tree abundance is high [
host fruit suitability (not examined here) could also be a factor.
Surprisingly, the moisture variables (e.g., mean annual precipitation and mean annual
summer precipitation; winter, spring, summer, and autumn relative humidity) were not among
the top predictors of R. pomonella abundance, suggesting the fly can tolerate environments
drier than predicted by laboratory experiments [20, 21]. However, proportion of developed
area, which include housing units and therefore human settlement and irrigation, was one of
the important metrics in the GLM model. This could mean that human settlement, and
therefore managed, irrigated habitats, could increase R. pomonella abundance by allowing host trees
to survive where otherwise they would not.
In addition to the developed category, shrub-scrub, cultivated crops, and pasture-hay
categories were also positively significant, even though fly abundance in the latter was low (Fig 2).
For shrub-scrub and pasture-hay categories, presence of an individual fly affected the analysis,
probably because the frequencies of sites sampled in these categories were low. One link across
shrub-scrub, cultivated crops, and pasture-hay categories may be that they are associated with
natural moisture or irrigation that allow host hawthorn or apple trees to survive. However,
the fact that shrub-scrub was the least significant of the three (P = 0.017) suggests that lack of
human intervention by way of irrigation or planting of host trees slightly reduces the chances
of large R. pomonella populations establishing.
Drivers of R. indifferens abundance
In contrast to R. pomonella, for R. indifferens most of the climatic variables had a positive
relationship with abundance. The top variables most positively related to R. indifferens abundance
in WA in the linear model were related to evapotranspiration and moisture deficit, i.e., drier
conditions. These results suggest dry conditions are more conducive to high R. indifferens
abundance than wetter conditions. Evapotranspiration is the sum of evaporation and plant
transpiration from the Earth?s land and ocean surface to the atmosphere, called the reference
evapotranspiration (ET0) [
]. Although these moisture deficit variables are not based on
11 / 17
soil moisture (or lack thereof) that would have the largest impact on fly survival, as 10?11
months of the fly?s life cycle are spent as a puparium in soil , air vapor deficits are positively
correlated with low soil moisture, particularly in arid and semi-arid regions [
with results here, soils do not need to be irrigated for survival of central WA-origin R.
indifferens . Furthermore, inspection of the distribution of R. indifferens abundance across WA
(Fig 4) indicates that fly abundance is relatively low in western WA where it is wet and high in
central WA where it is dry, visually supporting the statistical analyses.
Cherry host abundance was one of the significant variables in the GLM model, suggesting
areas that are hot and dry with the most unmanaged cherry trees will have the greatest R.
indifferens abundance. However, host abundance was not among the top variables related to R.
indifferens abundance in the linear model, suggesting that there are differences in fly
abundance given equal tree abundance between western and central WA. Cherry trees in western
WA seem abundant enough to support larger fly populations, yet fly populations there are
lower than in central WA. Drier climate positively affecting host fruit loads and other
ecological differences may be reasons. Under the relatively wet, humid, and warm winter climate in
western WA, cherry set may be lower than in central WA; trees may be subject to more fungal
growth, e.g., brown rot (Monilinia spp.) [
], and thus less healthy than trees in central
WA. Finally, birds may remove large proportions of the relatively few cherries in some western
WA trees, depriving flies of developmental sites (W. L. Y., personal observations).
The developed land-cover category was significant in the GLM model, as this variable, also
discussed in relation to R. pomonella above, is associated with residences, lawn grasses, and
thus irrigated habitat. Furthermore, as for R. pomonella, the cultivated crops category was
significant as well, probably because cultivated sweet cherry trees are common in this land-cover
class. This suggests the abundance of R. indifferens is dependent on the availability of sweet
cherries rather than irrigation per se. For example, if urban areas were planted with less
preferred host plants than sweet cherry, then perhaps the developed land-cover class would not be
significant due to lower host quality. If true, then the significance of the developed land-cover
class is more likely due to host plant quality than irrigation. The categories
grassland-herbaceous and wetlands were highly significant but negatively so, likely because most host trees are
unable to survive in these habitats.
Results indicate that planting of cultivated cherry trees in dry, hot irrigated habitat had
unanticipated consequences for pest management. The ability of R. indifferens to survive in
drier habitats with high sweet cherry abundance has increased the range and abundance of the
fly since pre-human settlement. There may be more R. indifferens now in WA than before
colonists from the eastern U.S. arrived with their cultivated cherry trees and began irrigating the
region in the mid-1800s .
Implications for fly quarantines and management
Our results can be used in fly management efforts. The goal for managing R. pomonella is to
keep it out of commercial apple orchards to maintain export markets [
]. There has never
been a report of a R. pomonella larva in commercially-packed apples from WA .
Implications for management of R. pomonella for apple export purposes include risk assessments for
apple orchards and establishment of Areas of Low Pest Prevalence or Pest Free Areas (ALPP
or PFAs) [
], which would allow movement of apples without cold treatment. Apple plantings
in areas with especially cold winters may have lower risk of being infested simply because of
low or no R. pomonella in those areas. In our study, apparently due in large part to low winter
temperatures, no R. pomonella were detected in Okanogan, Stevens, Pend Oreille, Asotin,
Garfield, Chelan and Walla Walla Counties. Washington State Department of Agriculture did
12 / 17
detect R. pomonella in Okanogan County in 2017  after our study, but our results suggest
the fly is unlikely to establish large populations there, so the county could in the future be
designated an ALPP for apple export purposes. Our study also suggests removal of unmanaged
host trees around orchards in moderately warm sites may limit fly abundance.
Rhagoletis indifferens is occasionally found infesting commercial cherries at low levels ,
due mostly to its high abundance in unmanaged trees near some cherry orchards. Unlike R.
pomonella, ALPP and PFAs do not seem possible for R. indifferens in the dry commercial
cherry-growing areas in central WA as it is abundant there. To reduce the threat of R.
indifferens invading and then infesting cherries in orchards, unmanaged cherry trees within 0.29 km
of cherry orchards (maximum dispersal distance of R. indifferens determined by Jones and
]) need to be removed. Cherry orchards themselves need to be properly managed using
approved insecticides applied on schedule; cherries remaining on trees postharvest need to be
sprayed with insecticides or removed [
]. Our findings do not alter these practices, but they
do show that they will almost certainly need to continue, and that any new cherry plantings in
drier areas in eastern WA where there are no flies now could be threatened in the future.
Summary and conclusions
In summary, we used primary survey data, biologically relevant environmental variables, and
modeling approaches to assess the abundance of two Rhagoletis pests in WA. Furthermore, we
explained the observed abundance and distribution patterns of these pests in relation to
climatic factors, host abundance, land-cover, and landscape metrics. Our results identify pest
abundant sites and can be used for understanding adaptation of insects to non-native and
native habitats and for aiding fly quarantine and management decisions. Experimental work
on proximate and ultimate causes of the relationships we established in this study will be
needed to more fully understand the intricacies of factors affecting fly population abundance.
S1 Fig. Relationships among environmental variables and R. pomonella abundance in
Washington State, U.S.A. (A) elevation had a negative relationship with R. pomonella
abundance while (B) winter mean minimum temperature (Tmin_wt), (C) winter degree days below
5?C (DD5_wt), and (D) frost-free period (FFP) had positive relationships with R. pomonella
S2 Fig. Relationship among environmental variables and R. indifferens abundance in
Washington State, U.S.A. Most of the climatic variables used in the analysis had a positive
relationship with R. indifferens abundance, suggesting that moisture deficit plays a
significant role in explaining the distribution of the pest in the state. (A) CMD = Hargreaves
climatic moisture deficit, (B) CMD_wt = winter Hargreaves climatic moisture deficit,
(C) SHM = summer heat-moisture index, (D) Eref = Hargreaves reference evaporation.
S1 Appendix. Land-cover classes and descriptions. The following land-cover categories were
found within 125 m radius of the trap locations.
S2 Appendix. ClimateWNA variables and descriptions. The following annual and seasonal
climate variables were obtained using ClimateWNA program?V.5.30.
13 / 17
We thank Shelly Watkins, Anne Kenny Chapman, Jennifer Stout, and Tessa Hansen
(USDAAgricultural Research Service) for assistance during the project. We thank the Temperate Tree
Fruit and Vegetable Research Unit, USDA-ARS, Wapato, WA, for providing the logistical
support. We also thank Erica Kistner-Thomas (USDA-ARS, Ames, IA) and Aaron Sidder
(Colorado State University, Fort Collins, CO) for reviewing early drafts of the manuscript and
anonymous reviewers who provided useful comments that further improved the manuscript.
Mention of trade names or commercial products in this publication is solely for providing
specific information and does not imply recommendation or endorsement by the U.S.
Department of Agriculture. USDA is an equal opportunity provider and employer.
Conceptualization: Wee L. Yee, Lisa G. Neven, Sunil Kumar.
Data curation: Tewodros T. Wakie.
Formal analysis: Tewodros T. Wakie.
Funding acquisition: Wee L. Yee, Lisa G. Neven, Sunil Kumar.
Investigation: Wee L. Yee, Lisa G. Neven, Sunil Kumar.
Methodology: Tewodros T. Wakie, Wee L. Yee, Lisa G. Neven.
Resources: Lisa G. Neven.
Software: Tewodros T. Wakie.
Supervision: Lisa G. Neven.
Visualization: Tewodros T. Wakie.
Writing ? original draft: Tewodros T. Wakie, Wee L. Yee.
Writing ? review & editing: Tewodros T. Wakie, Wee L. Yee, Lisa G. Neven, Sunil Kumar.
14 / 17
NASS. Value of Washington?s 2016 agricultural production totaled $10.6 billion. 2017;1?3. https://agr.
Hood GR, Yee WL, Goughnour RB, Sim S, Egan SP, Arcella T, et al. The geographic distribution of
Rhagoletis pomonella (Diptera: Tephritidae) in the western United States: Introduced or native
population? Ann. Entomol. Soc. Am. 2013; 106 (1): 59?65.
Sim SB, Doellman MM, Hood GR, Yee WL, Powell THQ, Schwarz D, et al. Genetic evidence for the
introduction of Rhagoletis pomonella (Diptera: Tephritidae) into the northwestern United States. J.
Econ. Entomol. 2017; 110(6): 2599?2608. https://doi.org/10.1093/jee/tox248 PMID: 29029209
Yee WL, Goughnour RB. Host plant use by and new host records of apple maggot, western cherry fruit
fly, and other Rhagoletis species (Diptera: Tephritidae) in western Washington state. Pan-Pac.
Entomol. 2008; 84 (3): 179?193.
Yee WL, Norrbom AL. Provisional list of suitable host plants of the apple maggot fly, Rhagoletis
pomonella (Walsh) (Diptera: Tephritidae). In: USDA Compendium of Fruit Fly Host Information (CoFFHI).
2017; Edition 2.0. https://coffhi.cphst.org
Frick KE, Simkover HG, Telford HS. Bionomics of the cherry fruit flies in eastern Washington. Wash.
Agric. Expt. Stn. Institute of Agr. Sci. Tech. Bull. 1954; 13: 1?66.
McClintock TG. Henderson Lewelling, Seth Lewelling and the Birth of the Pacific Coast Fruit Industry.
Oreg. Hist. Q. 1967; 68: 153?174.
Eide PM, Lynd FJ, Telford HS. The cherry fruitfly in eastern Washington. Wash. State Agric. Expt. Stn.
Circ. 1949; 72:1?8 (F).
Kumar S, Neven LG, Yee WL. Assessing the potential for establishment of western cherry fruit fly
(Rhagoletis indifferens) using ecological niche modeling. J. Econ. Entomol. 2014; 107 (2): 1032?1044.
Kumar S, Yee WL, Neven LG. Mapping global potential risk of establishment of Rhagoletis pomonella
(Diptera: Tephritidae) using MaxEnt and CLIMEX niche models. J. Econ. Entomol. 2016; 109 (5):
2043?2053. https://doi.org/10.1093/jee/tow166 PMID: 27452001
Lyons CP, Merilees B. Trees, shrubs, and flowers to know in British Columbia and Washington. Lone
Pine Publishing, Redmond, WA. 1995; 375 pp.
Neilson WTA. Some effects of relative humidity on development of pupae of the apple maggot,
Rhagoletis pomonella (Walsh). Can. Entomol.1964; 96: 810?811.
Trottier R, Townshend JL. Influence of soil moisture on apple maggot emergence, Rhagoletis
pomonella (Diptera: Tephritidae). Can. Entomol. 1979; 111: 975?976.
Yee WL. Soil moisture and relative humidity effects during post-diapause on emergence of western
cherry fruit fly (Diptera: Tephritidae). Can. Entomol. 2013; 145: 1?10.
Yee WL, Chapman PS. Irrigation and grass cover effects on pupal survival rates in soil and adult
emergence patterns of Rhagoletis indifferens (Diptera: Tephritidae). Environ. Entomol. 2018; 47(2): 457?
466. https://doi.org/10.1093/ee/nvx209 PMID: 29438537
Yee WL. Host plant use by apple maggot, western cherry fruit fly, and other Rhagoletis species (Diptera:
Tephritidae) in central Washington state. Pan-Pac. Entomol. 2008; 84 (3): 163?178.
Beyer HL. Hawth?s Analysis Tools for ArcGIS. 2004; http://www.spatialecology.com/htools
Goedickemeier I, Wildi O, Kienast F. Sampling for vegetation survey: Some properties of a GIS based
stratification compared to other statistical sampling methods. Coenoses. 1997; 12 (1):43?50.
Yee W L. Ammonium carbonate loss rates differentially affect trap captures of Rhagoletis indifferens
(Diptera: Tephritidae) and non-target flies. Can. Entomol. 2016; 149: 241?250.
USGS. 100-meter resolution elevation of the United States. 2012; https://nationalmap.gov/small_scale/
ESRI. 2016. ArcGIS Desktop: Release 10.4.1. Redlands CA.
Evans JS, Oakleaf J, Cushman SA. An ArcGIS toolbox for surface gradient and geomorphometric
modeling, version 2.0?0. 2014; https://github.com/jeffreyevans/GradientMetrics
Homer CG, Dewitz JA., Yang L, Jin S, Danielson P, Xian G, et al. Completion of the 2011 national
landcover database for the conterminous United States-representing a decade of land-cover change
information. Photogramm. Eng. Remote Sensing. 2015; 81 (5): 345?354.
Neilson WTA. Dispersal studies of a natural population of apple maggot adults. J. Econ. Entomol. 1971;
Senger SE. The dispersal of the western cherry fruit fly, Rhagoletis indifferens (Diptera: Tephritdae) in
structured environments [Ph.D. dissertation]. Burnaby (BC): Simon Fraser University; 2007.
Wang T, Hamann A, Spittlehouse DL, Murdock TQ. Climate WNA?High-resolution spatial climate data
for western North America. J. Appl. Meterol. Climatol. 2012; 51: 16?24.
15 / 17
Wang T, Hamann A, Spittlehouse D, Carroll C. Locally downscaled and spatially customizable climate
data for historical and future periods for North America. PLoS ONE. 2016; 11(6): e0156720. https://doi.
org/10.1371/journal.pone.0156720 PMID: 27275583
16 / 17
1. Mazzi D , Dorn S. Movement of insect pests in agricultural landscapes . Ann. Appl. Biology . 2012 ; 160 : 97 - 113 .
2. Raghuvanshi AV , Satpathy S , Mishra DS . Role of abiotic factors on seasonal abundance and infestation of fruit fly, Bactrocera cucurbitae (Coq.) on bitter gourd . J. Plant Protection Res . 2012 ; 52 : 264 - 267 .
3. Schurich J , Kumar S , Eisen L , Moore CG . Modeling Culex tarsalis abundance on the northern Colorado front range using a landscape-level approach . J. Am. Mosq. Control Assoc . 2014 ; 30 : 7 - 20 . https://doi. org/10.2987/ 13 - 6373 .1 PMID: 24772672
4. Manosathiyadevan M , Bhuvaneshwari V , Latha R . In: Dhanarajan A, editor. Sustainable Agriculture towards Food Security. Singapore , Springer; 2017 . p. 57 - 67 .
5. Kumar S , Simonson SE , Stohlgren TJ . Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park , CO, USA. Biodivers. Conserv. 2009 ; 18 : 739 - 763 .
6. Tews J , Brose U , Grimm V , Tielbo?rger K , Wichmann MC , Schwager M , et al. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures . J. Biogeogr . 2004 ; 31 : 79 - 92 .
7. Grez AA , Zaviezo T , Herna?ndez J , Rodr? ?guez -San Pedro A , Acu?a P. The heterogeneity and composition of agricultural landscapes influence native and exotic coccinellids in alfalfa fields . Agric. Forest Entomol . 2014 ; 16 : 382 - 390 .
8. Corcos D , Incla?n DJ , Cerretti P , Mei M , Di Giovanni F , Birtele D , et al. Environmental heterogeneity effects on predator and parasitoid insects vary across spatial scales and seasons: a multi-taxon approach . Insect Conserv. Divers . 2017 ; 10 : 462 - 471 .
36. Daly C , Halbleib M , Smith JI , Gibson WP , Doggett MK , Taylor GH , et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States . Int. J. Climatol . 2008 ; 28 : 2031 - 2064 .
West AM , Kumar S , Wakie T , Brown CS , Stohlgren TJ. , Laituri M , et al. Using high-resolution future climate scenarios to forecast Bromus tectorum invasion in Rocky Mountain National Park . PLoS ONE , 2015 ; 10 ( 2 ): e0117893. https://doi.org/10.1371/journal.pone. 0117893 PMID: 25695255
38. Sidder AM , Kumar S , Laituri M , Sibold JS . Using spatiotemporal correlative niche models for evaluating the effects of climate change on mountain pine beetle . Ecosphere . 2016 ; 7 : 1 - 22 .
39. Reich RM , Lundquist JE , Acciavatt RE . Influence of climatic conditions and elevation on the spatial distribution and abundance of Trypodendron ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) in Alaska . Forest Science . 2014 ; 60 : 308 - 316 .
40. R Development Core Team. R: a language and environment for statistical computing, R foundation for statistical computing . 2018 .
41. Akaike H. A new look at the statistical model identification . IEEE. Trans. Automat. Contr . 1974 ; 19 , 716 - 723 .
42. Aho K , Derryberry D , Peterson T. Model selection for ecologists: the worldviews of AIC and BIC . Ecology. 2014 ; 95 ( 3 ): 631 - 636 . PMID: 24804445
43. Jenks GF , Caspall FC . Error on choroplethic maps: Definition, measurement, reduction . Ann. Assoc. Am. Geo . 1971 ; 61 ( 2 ): 217 - 244 .
44. Smith JJ , Bush GL . Phylogeny of the Subtribe Carpomyina (Trypetinae), Emphasizing relationships of the genus Rhagoletis . In: Aluja M , Norrbom AL , editors. Fruit flies (Tephritidae): Phylogeny and evolution of behavior . Boca Raton: CRC Press. 1999 ; 187 - 217 .
45. Dean RW , Chapman PJ . Bionomics of the apple maggot in eastern New York . Search Agriculture. 1973 ; 3 ( 10 ): 1 - 62 .
46. Climate Data 2018a . Climate Ithaca-New York. https://www.usclimatedata.com/climate/ithaca/newyork/united-states/ usny0717
Western Regional Climate Center . Climate summaries. 2018 ; https://wrcc.dri.edu/Climate/summaries.
48. Climate Data 2018b . Climate Hudson-New York. https://www.usclimatedata.com/climate/hudson/ new-york/united-states/ usny0692
49. Zhang Y , Wand S , Barr AG , Black TA . Impact of snow cover on soil temperature and its simulation in a boreal aspen forest . Cold. Reg. Sci. Technol . 2008 ; 52 : 355 - 370 .
50. Yee WL , Klaus MW , Cha DH , Linn CE Jr, Goughnour RB , Feder JL . Abundance of apple maggot, Rhagoletis pomonella, across different areas in central Washington, with special reference to black-fruited hawthorns . J. Insect Sci . 2012 ; 12 ( 124 ): 1 - 14 .
51. Yee WL , Lawrence TW , Hood GR , Feder JL . New records of Rhagoletis Loew, 1862 (Diptera: Tephritidae) and their host plants in western Montana, U.S.A . Pan-Pac . Entomol . 2015 ; 91 ( 1 ): 39 - 57 .
52. Hargreaves GH . Defining and using reference evapotranspiration . J. Irrig. Drain. Eng . 1994 ; 120 ( 6 ): 1132 - 1139 .
53. Hargreaves GH , Allen RG . History and evaluation of Hargreaves evapotranspiration equation . J. Irrig. Drain. Eng . 2003 ; 129 ( 1 ): 53 - 63 .
54. Hargreaves GH , Samani ZA . Reference crop evapotranspiration from temperature . Appl. Eng. Agric . 1985 ; 1 ( 2 ): 96 - 99 .
55. McHugh TA , Morrissey EM , Reed SC , Hungate BA , Schwartz E. Water from air: an overlooked source of moisture in arid and semiarid regions . Sci. Rep . 2015 ; 5 : 1 - 6 .
56. Eastwell KC , Grove GA , Johnson DA , Mink GI , Byther RS , Covey RP , et al. Field guide to sweet cherry diseases in Washington . Washington State University Extension. EB 1323E . 2005 ; http:// www.co.chelan.wa.us/files/horticultural-pest-and - disease-board/documents/DiseasesofCherries. pdf
57. Oliveira LL , Pacheco I , Mercier V , Faoro F , Bassi D , Bornard I , et al. Brown rot strikes Prunus fruit: an ancient fight almost always lost . J. Agric. Food Chem . 2016 ; 64 ( 20 ): 4029 - 4047 . https://doi.org/10. 1021/acs.jafc. 6b00104 PMID: 27133976
58. Yee WL . Seasonal distributions of eggs and larvae of Rhagoletis indifferens (Diptera: Tephritidae) in cherries . J. Entomol. Sci . 2005 ; 40 : 158 - 166 .
59. Maxwell SA , Thistlewood HMA , Keyghobadi N. Population genetic structure of the western cherry fruit fly Rhagoletis indifferens (Diptera: Tephritidae) in British Columbia, Canada . Agric. Forest Entomol. 2014 ; 16 : 33 - 44 .
60. IPPC. Establishment of areas of low pest prevalence for fruit flies (Tephritidae) . Food and Agriculture Organization of the United Nations . 2017 ; Doc # ISPM 30 . https://www.ippc.int/en/publications/589/ 61.
WSDA. Proposed rulemaking . CR- 102 ( December 2017 ). Implements RCW 34.05.320 . 2018 ; 1 - 3 .
WSDA. 2017 industry cherry workshop. Fruit & Vegetable Inspection Program . 2017 ; https://agr.wa.
63. Jones SC , Wallace L . Cherry fruit fly dispersion studies . Journal of Economic Entomology . 1955 ; 48 : 616 - 617 .
64. Messina FJ , Smith TJ . Western cherry fruit fly . Rhagoletis indifferens Curran (Diptera: Tephritidae) . 2010 ; http://treefruit.wsu.edu/crop-protection/opm/western-cherry - fruit-fly/