A Pest Risk Assessment for Copitarsia Species:
A Case Study in Support of a Risk Based
Resource Allocation Model
October 2000
Juli Gould: Phoenix Plant Protection Center, Phoenix, AZ
Robert Venette: Raleigh Plant Protection Center, St. Paul, MN
Joseph Davidson: State Plant Health Director, Auston, TX
Philip Kingsley: Otis Plant Protection Center, Falmouth, MA
Risk Assessment of Copitarsia spp.
EXECUTIVE SUMMARY
This case study focuses on the genus Copitarsia (Lepidoptera [moths]: Noctuidae), which
are often detected as larvae on commodities arriving from Mexico, Central America, and
South America. The objectives of the short-term project were to 1) conduct a thorough
pest risk assessment for Copitarsia; 2) document information requirements to assess pest
risk; 3) identify appropriate sources of information; 4) highlight critical data gaps; and
5) synthesize available information in a way that might be useful for a risk-based resource
allocation model.
The goal of basing resource allocation at ports of entry on risk is an admirable one, and a
critical goal in the face of increasing commodity trade without concomitant increases in
port staffing levels. However, creating a risk based resource allocation model assumes that
APHIS can adequately define the risk that commodities and pathways pose to American
agriculture and natural systems. As reported in a recent review of the APHIS-PPQ
Safeguarding System, “…a major obstacle to the evolution of the APHIS pest risk analysis
process has been, and remains, the lack of reliable data. In the absence of robust data,
APHIS relies on a process that analyzes potential pest introductions based largely on
highly subjective and uncharacterized expert judgment in the assessment of risk values.
Yet, reliable information is critical to understanding and predicting invasion threats,
evidencing necessity of phytosanitary measures, and managing resources effectively and
efficiently” (National Plant Board 1999). We have attempted to overcome this problem by
collecting all available data and clearly documenting the basis for all conclusions. In
addition, we have surveyed a number of experts to formally characterize variability and
uncertainty in the assignment of risk ratings. Where appropriate, we have made
recommendations for future data collection.
Highlights
The genus Copitarsia
 Taxonomy within the genus is poor. Consequently, pest reports or biological studies
cannot be reliably assigned to any one species. Hence, our assessment focuses on the
genus as a whole.
 Some members of Copitarsia may not be pests. In the literature, only 4 species have
been reported as adversely affecting commercial crops.
 The genus is highly polyphagous. At least 39 crop plants from 19 different plant
families have been reported as hosts. Copitarsia has been intercepted on 150 genera of
plants at U.S. ports of entry.
 In its native range, Copitarsia may complete 1-4 generations per year. A female moth
may produce as many as 1,600 eggs depending on host quality.
Probability of arrival
 In its native range, a suite of biological control agents attack Copitarsia and may
naturally suppress densities of the insect. Cultural practices designed to intensify
production may disrupt these biological controls and contribute to pest outbreaks thus
increasing the risk of viable larvae being sent to the U.S.
 Over the past 15 years, Copitarsia has been most commonly detected at Miami, FL,
and Laredo and Hidalgo, TX. Few interceptions of Copitarsia were made at Nogales, AZ
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USDA-APHIS
or San Diego, CA where significant volumes of agricultural imports arrive. The total
number of Copitarsia detected at a port could not be explained simply by the number of
shipments that arrive at that port from other countries in the Western Hemisphere.
 Copitarsia were most frequently intercepted on commodities arriving from Colombia
or Mexico. Differences in the number of interceptions on commodities from different
countries could be explained by the number of shipments imported from those countries
into the U.S.
 Copitarsia were most commonly observed on cole crops (Brassica spp.) and cut
flowers, particularly sea lavender (Limonium spp.) and lily of the Incas (Alstroemeria spp).
 Copitarsia are most likely to arrive in March and April.
 Common sampling practices at ports of entry (2% of a shipment) are not likely to
detect pests with 95% confidence. When shipments are relatively small, the degree of
infestation may be relatively high and the shipment still allowed to enter the U.S.
Sampling under Agriculture Quarantine Inspection Monitoring guidelines achieves a high
level of detecting the pest but generally requires that a greater number of samples are
collected.
Probability of establishment
 Climate matching and biological models indicate that the probability of Copitarsia
establishment is 90% in California, Oregon, and Washington and is between 50 and 70%
in western Idaho. Each model gives a slightly different degree of risk but both models
suggest that the probability of establishment is >50% in portions of New Mexico, Texas,
Oklahoma, Arkansas, Missouri, Kentucky, Tennessee, Georgia, South Carolina, North
Carolina, Virginia, West Virginia, Maryland, Florida, Pennsylvania, New Jersey, Indiana,
and Illinois.
 Potential hosts (both wild and cultivated plants) occur widely throughout the US.
 Establishment of Copitarsia does not necessarily mean that the insect will outbreak and
cause disastrous economic or ecological effects. Conceivably, populations could remain at
low densities for years.
Ability to spread within the U.S.
 Dispersal of Copitarsia, either as larvae or adults, is poorly described in the literature.
 In general, many adult noctuids are capable of utilizing prevailing winds to disperse
great distances (>500 mi).
 Copitarsia are likely to be moved throughout the U.S. if infested commodities are
shipped to different parts of the country.
Consequences of Establishment
 Copitarsia damages crops directly by reducing the marketability of fruits and
vegetables and indirectly by reducing overall yields. In South America, Copitarsia
reduced marketability of certain vegetables by 24% and yields of grains by 80-90%.
 If Copitarsia populations were to outbreak in the U.S., use of broad-spectrum
insecticides seems likely. Non-target impacts seem probable.
 Biological control programs based on the release of foreign antagonists are likely to be
pursued if Copitarsia becomes established in U.S.
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Risk Assessment of Copitarsia spp.
Overall Risk Assessment and Uncertainty Analysis
 We provided experts with a summary of the available literature on Copitarsia and
APHIS guidelines for risk assessment. Each panel member was asked to provide ratings
(high, medium, or low) for each of APHIS’ risk assessment elements and to indicate their
level of confidence (high, medium, or low) in their assessment.
 Overall, the panel (n=8 at the time of this summary) felt Copitarsia had a high
probability of invading the US and a high probability of having adverse effects but
confidence in these assessments was moderate to low.

The likelihood of Copitarsia introduction evaluated on several risk elements:
Risk
Confidence
Element
(% of respondents) (% of respondents)
Quantity of commodity imported
High (100%)
High (100%)
annually
Likelihood survive post-harvest
High (62%)
Low (50%)
treatment
Likelihood survive shipment
Medium (50%)
Low (88%)
Likelihood not detect at port of entry
High (75%)
High (75%)
Likelihood moved to suitable habitat
High (62%)
High/Low (38% each)
Likelihood find suitable host
High (75%)
Medium (50%)
Overall
High
Low

The consequences of Copitarsia introduction evaluated on several risk elements:
Risk
Confidence
Element
(% of respondents) (% of respondents)
Climate/Host
High (62%)
Medium (62%)
Host Range
High (100%)
High (62%)
Dispersal Potential
High (62%)
Medium (62%)
Economic Impact
High (88%)
High (62%)
Environmental Impact
High (100%)
Med-Low (38% each)
Overall
High
Medium
Future Study
 Improved taxonomy of the genus is critical to fully assess risks that individual species
may pose. Not all of the recognized species of Copitarsia have been reported as pests.
 Evaluating the influence of climate (particularly temperature and moisture) on
dynamics of Copitarsia will improve predictions of outbreaks in its native range, more
precisely identify regions of the U.S. that are likely to provide suitable habitat, and more
accurately estimate mortality as Copitarsia is shipped to and throughout the U.S.
 The distribution of Copitarsia has, at best, been described in a piecemeal fashion in the
literature. Distribution information is vital to identify potential source regions and to
verify that Copitarsia is not yet established in the U.S.
 Additional assessments of widely different organisms (e.g., pathogens, weeds, and
mites) will be important to determine which factors (i.e., risk elements) might be adjusted
in a dynamic way to provide origin/commodity/port-specific assessments of risk.
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Risk Assessment of Copitarsia spp.
TABLE OF CONTENTS
EXECUTIVE SUMMARY ........................................................................................................... I
INTRODUCTION ......................................................................................................................... 3
THE GENUS COPITARSIA........................................................................................................ 4
LIFE CYCLE AND TAXONOMY OF THE GENUS COPITARSIA ..............................................................................4
CURRENT RANGE OF COPITARSIA ....................................................................................................................5
PROBABILITY OF COPITARSIA ENTERING THE UNITED STATES ............................. 7
POTENTIAL DENSITIES OF COPITARSIA ON IMPORTED COMMODITIES OR CONVEYANCES ..................................7
FREQUENCY OF ARRIVAL AT BORDER OR OTHER DETECTION SITES ............................................................ 10
QUANTITY OF IMPORTS ON WHICH COPITARSIA COULD OCCUR .................................................................... 16
PROBABILITY THAT COPITARSIA WILL NOT BE DETECTED AT THE PORT OF ENTRY ....................................... 20
LIKELIHOOD OF NATURAL DISPERSAL INTO THE UNITED STATES................................................................. 22
COMMODITY TREATMENTS THAT COULD REDUCE THE RISK OF INVASION................................................... 23
PROBABILITY OF ESTABLISHMENT IN THE UNITED STATES ................................. 24
POTENTIAL RANGE IN USA BASED ON CLIMATIC TOLERANCE ..................................................................... 24
HOST PLANT RANGE ..................................................................................................................................... 30
Crop Plants .............................................................................................................................................. 31
Closely Related Native Species ................................................................................................................ 38
Crop Phenology and Susceptible Stages .................................................................................................. 38
IS COMMODITY PROCESSED IN SOME WAY THAT WOULD REDUCE PEST RISK? ........................................... 39
ABILITY TO SPREAD WITHIN THE UNITED STATES AFTER
ESTABLISHMENT .................................................................................................................... 40
NATURAL SPREAD ........................................................................................................................................ 40
MOVEMENT WITH COMMODITIES .................................................................................................................. 40
POTENTIAL FOR DAMAGE IF ESTABLISHED (POTENTIAL ECONOMIC
IMPORTANCE) .......................................................................................................................... 41
BIOTIC POTENTIAL ........................................................................................................................................ 41
Number of Generations Per Year ............................................................................................................. 41
Fecundity .................................................................................................................................................. 41
Generation Time....................................................................................................................................... 41
Mortality................................................................................................................................................... 41
Reproduction Method (asexual, sexual) ................................................................................................... 42
Intrinsic Rate of Increase ......................................................................................................................... 42
TYPE AND EXTENT OF DAMAGE .................................................................................................................... 42
EFFECT ON EXPORT MARKETS ...................................................................................................................... 46
EFFECT ON CONTROL COSTS AND ON ONGOING IPM PROGRAMS ................................................................. 47
ENVIRONMENTAL DAMAGE (DIRECT AND INDIRECT) ................................................................................... 47
CAPACITY TO ACT AS A VECTOR FOR OTHER PESTS OR DISEASES ................................................................ 47
RISK ASSESSMENT .................................................................................................................. 48
CONSEQUENCES OF INTRODUCTION .............................................................................................................. 50
Climate/Host ............................................................................................................................................ 50
Host Range ............................................................................................................................................... 50
Dispersal Potential ................................................................................................................................... 51
Economic Impact ...................................................................................................................................... 51
Environmental Impact .............................................................................................................................. 51
LIKELIHOOD OF INTRODUCTION .................................................................................................................... 52
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USDA-APHIS
Quantity of Commodity Imported Annually ............................................................................................. 52
Likelihood Survive Post-harvest Treatment ............................................................................................. 53
Likelihood Survive Shipment ................................................................................................................... 53
Likelihood not Detect at Port of Entry..................................................................................................... 53
Likelihood Moved to Suitable Habitat ..................................................................................................... 53
Likelihood find Suitable Host .................................................................................................................. 54
OVERALL ASSESSMENT OF RISK................................................................................................................... 54
AREAS RECOMMENDED FOR FUTURE STUDY ............................................................. 55
NEEDED TO REFINE COPITARSIA RISK ASSESSMENT AND MANAGEMENT: ...................................................... 55
NEEDED TO REFINE RISK-BASED RESOURCE ALLOCATION: ........................................................................... 55
ACKNOWLEDGMENTS .......................................................................................................... 56
APPENDIX I: PHOTOGRAPHIC AID FOR IDENTIFICATION OF COPITARSIA
LARVAE ...................................................................................................................................... 57
SPECIES OFTEN CONFUSED WITH COPITARSIA................................................................................................ 58
TAXONOMIC AND SYSTEMATIC CHALLENGES OF COPITARSIA ...................................................................... 58
ROLE OF SYSTEMATICS IN AGRICULTURE ..................................................................................................... 59
RECOMMENDATIONS .................................................................................................................................... 59
APPENDIX III: REFERENCES CITED TO DETERMINE GEOGRAPHIC
DISTRIBUTION OF COPITARSIA.......................................................................................... 61
APPENDIX IV: METHODS USED FOR CLIMEX ANALYSIS ......................................... 63
APPENDIX V: DETAILED RESULTS OF CLIMEX ANALYSIS ..................................... 67
APPENDIX VI: PRODUCTION OF INDIVIDUAL CROPS BY COUNTY WITHIN
THE UNITED STATES ............................................................................................................. 72
APPENDIX VII: NATURAL ENEMIES ATTACKING COPITARSIA IN ITS
NATIVE RANGE........................................................................................................................ 76
REFERENCES ............................................................................................................................ 80
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Risk Assessment of Copitarsia spp.
INTRODUCTION
Noctuid moths in the genus Copitarsia are frequently intercepted on produce and cut
flowers at U.S. border ports and are considered actionable pests. If Copitarsia larvae are
found in a shipment, the commodity must either be treated or returned to the country of
origin. Officers working for the Animal and Plant Health Inspection Service (APHIS),
Plant Protection and Quarantine (PPQ) are encouraged to inspect 2% of incoming
shipments. Copitarsia larvae are often found at ports in Texas and Florida; they are rarely
encountered at other ports in Arizona and California, yet officers in Arizona and California
still spend much of their resources looking for this pest. APHIS has recently embarked on
a long-term project to develop an expert system that will assist port supervisors in making
resource allocations based on the risk of pest invasion and economic and environmental
damage. An auxiliary goal of the project is to allocate resources among ports based on
risk. A short-term goal to support the overall project was to look at the risk posed by
Copitarsia, based on the probability that it will enter the United States, become
established, and cause economic damage.
Before one can assess risk, one must gather data on which to base the assessment. We
accessed many sources of information to assess pest risk. These sources include published
literature, Internet databases (PLANTS, Economic Research Service FOCUS, Texas
Agriculture Statistics Service), web pages, APHIS databases (PIN-309, PQ-280, AQIM),
CD-ROM databases (National Agriculture Statistics Service, CABI Crop Protection
Compendium), internal APHIS reports, and APHIS port manuals. We also contacted
several experts in the field of Noctuid taxonomy via email. Much of the published
literature was in Spanish. Since none of the authors is fluent in Spanish, we took two
approaches. One author hired a research associate who was fluent in Spanish to
summarize the literature, while another author purchased translation software and had
technicians who knew Spanish proofread the documents.
We were more confident in the adequacy of some data used to assess risk than others.
Traditional APHIS risk assessments describe risk by consensus of a scientific panel.
Generally, differences of opinion among panel members or the degree of confidence in the
assessment are not described. We strongly feel that any risk based resource allocation
model or expert system must not only account for estimations of risk but also for the
amount of variability and certainty in those assessments. This is because there will always
be gaps in the available data, and risk assessments will, by default, need to be based on
incomplete data, at least initially.
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USDA-APHIS
THE GENUS COPITARSIA
Life Cycle and Taxonomy of the Genus Copitarsia
Copitarsia spp. (Lepidoptera: Noctuidae) are moths within the subfamily Cuculliinae. The
genus was established by Hampson in 1906. Copitarsia begin the life-cycle as eggs
deposited either singly (C. consueta: Lopez-A. 1996a) or in egg masses (C. turbata:
Velasquez-Z. 1988). The larvae have 5-6 instars (Arce de Hamity and Neder de Roman
1993, Lopez-A. 1996a), and reach a length of approximately ¾ to 1 ½ inches. The two
final larval instars consume the most and therefore cause the most damage. The larvae
tend to be green in color, but Lopez-Avila (1996a) reports green, black, and gray phases
that also vary in habitat and crops attacked. Larvae possess dark
bars at the base of the two medial setae and white dorsal setae
(Riley 1998b) (See Appendix I for some larval keys used by PPQ
officers). These setae on the head do not align dorsal ventrally.
Larvae also have two dark triangles on posterior abdominal
segments, and possess a dark pattern on the head capsule. Larvae
also have a flat spinneret and a short spine on the labial palp.
Copitarsia pupates in the soil, and the pupae are brown and
typical of noctuids. Copitarsia are gray or brown moths that
possess crests on the prothorax and metathorax (Hampson 1906)
(Fig. 1) and are difficult to distinguish from other noctuids. The
forewing is rectangular at the base and rounded distally.
Figure 1: Adult noctuids: 27: C. humilis, 28: C. consueta, 29: C. naenoides, 30: C.
patagonica, 31: C. purilinea (Hampson 1906).
Over time, the genus Copitarsia has included from six to eleven species, depending on
which taxonomic authority is consulted (Table 1). Hampson included six species in
Copitarsia: C. humilis (Blanchard), C. consueta (Walker), C. turbata (Herrich-Schäffer)
(the type species), C. naenoides (Butler), C. patagonica Hampson, and C. purilinea
(Mabille) (Table 1). Subsequently, Köhler (1959) described C. basilinea, bringing the total
number of species to seven. Poole (1989) found C. consueta to be a taxonomically invalid
name and designated a replacement name of C. incommoda. Poole also transferred C.
editae (Angulo et al. 1985) from Euxoa, bringing the total number of species in Copitarsia
to eight. Castillo and Angulo (1991) redescribed and revised Copitarsia, using both adult
and immature stages as sources of characters. In this revision, Castillo and Angulo
transferred C. clavata (Köhler) from Cotarsina. The authors also described two new
species: C. anguloi Castillo and C. paraturbata Castillo and Angulo. In its history,
Copitarsia has been associated with several noctuid genera belonging in two other
subfamilies: Euxoa (Noctuinae), Polia (Hadeninae), Noctua (Noctuinae), Mamestra
(Hadeninae), Spaelotis (Noctuinae), Agrotis (Noctuinae), Graphiphora (Noctuinae),
Orthosia (Hadeninae), and Xestia (Noctuinae) (Table 1).
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Risk Assessment of Copitarsia spp.
Table 1. List of species currently included in Copitarsia, genera to which they were
originally assigned, and their synonyms (* = reported as pest species).
Species Author date
C. anguloi Castillo Angulo 1991
C. basilinea Köhler 1959
C. clavata (Köhler) 1951
C. editae Angulo & Jana-Saenz 1982
* C. humilis (Blanchard) 1852
* C. incommoda (Walker) 1865
Original genus
Copitarsia
Copitarsia
Cotarsina
Euxoa
Polia
Agrotis
* C. naenoides (Butler) 1882
C. paraturbata Castillo & Angulo 1991
C. patagonica Hampson 1906
C. purilinea (Mabille) 1885
* C. turbata (Herrich-Schäffer) 1885
Anomogyna
Copitarsia
Copitarsia
Orthosia
Polia
Synonym Author date
Agrotis consueta Walker 1857
Agrotis peruviana Walker 1865
Lycophotia margaritella Dogin 1916
Mamestra docolora Guenée 1852
Mamestra inducta Walker 1856
Spaelotis subsignata Walker 1857
Agrotis hostilis Walker 1857
Graphiphora sobria Walker 1857
Proper identification and knowledge of the systematics of Copitarsia is key to its control.
Of the eleven species currently classified as Copitarsia, only four species (C. incommoda,
C. humilis, C. naenoides, C. turbata) are reported to be pests in the literature (with C.
incommoda called C. consueta). Because Copitarsia has not been examined in a modern
phylogenetic framework, it is possible that these names represent geographic variants of
one or two species. Also, larval associations with adults have not been made for members
of Copitarsia. As a result, benign species may be mistaken for pest species, and may result
in the rejection of a commodity for importation, which is a waste of time and money.
Additionally, members of Copitarsia may be currently included in other genera, perhaps in
other subfamilies (i.e. Agrotis). A modern systematic treatment of Copitarsia is needed to
improve pest identification and risk management (Appendix II).
Current Range of Copitarsia
Species in the genus Copitarsia can be found along the western edge of South and Central
America from the southern tip of Argentina north through central Mexico (Fig. 2:
Appendix III). There are no records in the literature of Copitarsia existing in Brazil,
Paraguay, Uruguay, Suriname, French Guiana, Guyana, Belize, Nicaragua, Panama, El
Salvador, or Honduras. However, the PIN-309 interception database shows Copitarsia
arriving at the U.S. border on commodities from Belize, Brazil, Costa Rica, El Salvador,
Guatemala, Honduras, Nicaragua, and Panama. It is not known if the commodities were
harvested in those countries or simply shipped from packing facilities there. Copitarsia
has also been recovered in commodity shipments from several Caribbean islands: the
Dominican Republic, Haiti, Jamaica, St. Lucia, and Trinidad and Tobago. A lack of
records in the literature from these countries does not necessarily mean that Copitarsia is
5
USDA-APHIS
not present. Some possible explanations are 1) Copitarsia simply may not reach high
densities or damage crops and is therefore ignored, 2) scientific expertise in those countries
may be a lacking, 3) scientists may not publish results in readily accessible journals, or 4)
the climate is marginal for Copitarsia. In much of its range, Copitarsia species are
thought to be kept in check by natural enemies (Artigas and Angulo 1973a; Cortes 1976;
Hichins-O. and Mendoza-M. 1976; Cortes-P. et al. 1972; Vimos-N. et al. 1998; Apablaza
and Stevenson 1995). Figure 2 presents the minimum range of Copitarsia, with the actual
range almost certainly including the countries between Mexico and South America and
possibly islands in the Caribbean as well.
Figure 2: Current distribution of Copitarsia as described in the literature.
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Risk Assessment of Copitarsia spp.
PROBABILITY OF COPITARSIA ENTERING THE UNITED STATES
In its native range, a species may be associated with a number of commodities or
conveyances. When those commodities or conveyances are associated with international
trade, they become pathways by which the species may arrive in the US. In this section,
we attempt to identify primary pathways, both natural and trade-related, by which
Copitarsia could arrive in this country.
Potential densities of Copitarsia on imported commodities or conveyances
All biological invasions begin with a species in its native range. In general, the number of
individuals that are likely to survive shipment to a foreign country depends on the number
of individuals that were originally sent. Currently, the number of Copitarsia that are
shipped to the U.S. is not known. The relative number of Copitarsia sent from different
Western Hemisphere countries might be inferred from literature reports of the frequency
and severity of Copitarsia outbreaks. We consider an outbreak to have occurred when
significant economic losses due to Copitarsia are reported. A search of the published
literature uncovered 21 references that discuss the economic status and frequency of
outbreaks of Copitarsia in its native range (Table 2). Copitarsia was reported to cause
high economic damage in Argentina, Bolivia, Columbia, Peru, and Mexico (from where
we might anticipate greater densities of insects sent to the U.S.), and little damage in
Ecuador and Peru (from where we might expect lower Copitarsia densities). In Chile,
nearly an equal number of reports indicated high or low damage from the pest. In six of
the cases where damage was described as high, the authors attributed high pest densities to
the activities of man. The use of insecticides that released Copitarsia from control by
natural enemies and other cultivation practices (monocultures, removing weeds, and
planting at low density) were thought to favor the development of pest populations (Table
2). For example, in Chile, Copitarsia was not considered a pest of artichokes harvested
early for local consumption, but it was a problem in exported artichokes that were
harvested later in the year when Copitarsia populations had a chance to build (Table 2).
Although populations of Copitarsia can reach high densities, outbreaks of Copitarsia are
often considered infrequent (Table 2). Weather, particularly temperature and rainfall,
seems to affect the survival of over-wintering pupae, with favorable conditions leading to
high densities of adults during the spring, and thus to outbreaks (refer to Table 2).
Research to more clearly define conditions that promote outbreaks of Copitarsia would be
useful to anticipate densities of insects that might be sent to the U.S. That information,
combined with weather data from South and Central America, would be useful to generate
real-time predictions of Copitarsia outbreaks. PPQ officers could be more vigilant at
border ports for commodities coming from countries where an outbreak of Copitarsia is
likely to be occurring.
7
Table 2: Economic status and frequency of outbreaks of Copitarsia in its native range.
Suspected
Cause of
Outbreak
Bolivia
potato
C. consueta Listed as the most troublesome tuber pest.
High
Bolivia
quinoa
C. turbata
Can cause up to 80-90% loss if not controlled - conclude that
changes in agricultural practices have led to pest outbreaks.
High
Chile
alfalfa
C. turbata
Did not cause detectable damage - diverse entomophagous
insects contributing to control.
Low
Chile
alfalfa
C. consueta Up to 5 million noctuids/ha - noctuids kept below economic
+ humilis
threshold by parasites when no insecticide applied - when
apply insecticides must apply 12-15 times/year.
High
Chile
alfalfa
C. humilis
Low
Infrequent
(Hichins-O. and
Mendoza-M. 1976)
Chile
alfalfa
C. consueta Low density during the year with the lowest density in the
spring.
Low
Infrequent
(Hichins-O. and
Rabinovich 1974)
Chile
artichoke C. consueta Copitarsia damaged up to 24% of marketable small heads only a problem for export because of rejection at the US ports
- damage not severe during harvest season for local
consumption.
Chile
onion
Chile
quinoa
8
Country Crop
Argentina lettuce
Copitarsia
spp.
C. turbata
Irregular or occasional occurrence - always in a low density
of population possibly regulated by natural enemies.
Damage
Low/High
High
Damage
Frequent/
Infrequent
Copitarsia
Species
Pest Status
C. turbata Can cause severe damage.
Infrequent
High
(Liberman-Cruz
1986)
(Apablaza and
Stevenson 1995)
Agricultural
Practices
High
Only occasional outbreaks.
Irregular but sometimes severe damage.
Agricultural
Practices
References
(Arce de Hamity
and Neder de
Roman 1993)
(Munro 1954)
Agricultural
Practices
(Cortes 1976) and
(Cortes-P. et al.
1972)
(Machuca-L et al.
1989)
Infrequent
(Quiroz-E. 1977)
Infrequent
(Lamborot et al.
1999)
Country Crop
Copitarsia
Species
Pest Status
Damage
Low/High
Damage
Frequent/
Infrequent
Suspected
Cause of
Outbreak
Frequent
Agricultural
Practices
Chile
rapeseed C. consueta Frequently explosive increases in populations take place. 90100 larvae per plant – lack of insecticides allows populations
of parasites and disease to be maintained.
High
Chile
various C. naeniodes Listed as a prevalent detrimental insect.
High
Chile
various C. turbata
Columbia garlic
and
onion
Columbia potato
Columbia potato
9
potato
Weather
Weather
(Lopez-A. 1996a)
High
Weather
(Zenner de Polenia
1990)
Copitarsia
spp.
C. turbata
Except for rare cases are not of importance. In most cases
controlled with natural enemies.
Percent damaged plants approximately 45%.
Low
Infrequent
(Larrain 1998)
High
cabbage Copitarsia
spp.
cabbage Copitarsia
spp.
Peru
Infrequent
C. consueta Severe attacks occur in dry periods when potato plants are
young.
C. consueta Can cause economic damage, but mainly after severe dry
seasons and only during the first month after germination.
Mexico
potato
(Duran-M 1972)
Low
melloco C. turbata
Peru
(Artigas and
Angulo 1973a)
C. consueta Found at low incidence.
Ecuador
Mexico
Outbreaks happen infrequently, but related to environmental
conditions such as rainy winters free of frosts - leads to high
survival of overwintering pupae.
References
(Lopez-A. 1996b)
Infrequent
(Vimos-N. et al.
1998)
(Carrillo-S. 1971)
High
Cultivation practices (monoculture, weed free areas, low
planting densities, application of insecticides) favor the
development of pest populations.
High
Losses from Copitarsia are relatively low and considered a
secondary pest.
Density was variable.
Low
Agricultural
Practices
(Monge-V. et al.
1984)
Infrequent
(Arestegui-P. 1976)
Infrequent
(Leyva-O. and
Sanchez-V. 1993)
USDA-APHIS
Frequency of Arrival at Border or Other Detection Sites
Pest interception records provide an indication of the frequency of arrival of certain pests.
These frequencies may be useful to assess the relative risks of Copitarsia arriving at a
particular port on certain host plants from a given country. An analysis of Copitarsia
interception records was conducted to answer the following questions: 1) which ports
most frequently report interceptions of Copitarsia? 2) Which countries are most
commonly reported as the origin of Copitarsia infested goods/commodities? 3) Which
pathways (i.e., baggage, mail, cargo, stores, quarters, or holds) are cited most often when
reports of Copitarsia interceptions are made? 4) On which goods/commodities are
interceptions of Copitarsia most frequently noted? and 5) Are reported interceptions of
Copitarsia more common during a particular month or season?
The analysis was based on information available in the Port Interception Network (PIN)309 database. The database was originally queried for all taxa of Copitarsia detected
between January 1985 and April 2000. Four taxa were identified: Copitarsia sp., C.
consueta, C. incommoda, and C. turbata. Separate summary reports were generated for
each taxon. Five informational fields were included in summary reports: port, interception
date (reported as month and year), host, method of conveyance (i.e., mail, cargo, etc.), and
country of origin. The total number of interceptions with identical information in all fields
was also provided. The four summary tables were combined into one master table for
numerical and statistical analysis.
Simple data summaries were performed in Excel, and statistical analysis was conducted in
SAS. Because interception reports are not based on random sampling nor are sampling
procedures identical from port to port, traditional parametric statistics (e.g., t-tests and
ANOVA) are not appropriate. Consequently, monthly dynamics of pest interception
records were analyzed using non-parametric statistics (from PROC NPAR1WAY) based
on rank transformed data. Means were used for ties during the ranking procedure (based
on total interceptions per month at each of the 15 most commonly reporting ports).
Ranked monthly interceptions were analyzed using non-parametric ANOVA with means
separation by the Ryan-Einot-Welsh multiple range test.
In an attempt to validate observed trends, we obtained additional pest interception records
from Agriculture Quarantine Inspection and Monitoring (AQIM) samples. We focused on
inspections of air and border cargo arriving from 1 November 1996 to 30 November 1999.
Southern border ports participating in AQIM sampling of cargo included Nogales and San
Luis (AZ), Calexico (CA), Brownsville, El Paso, Hidalgo, Laredo, and Pharr (TX). Ports
participating in AQIM sampling of air cargo included Los Angeles (CA), Miami, Tampa
(FL), Atlanta (GA), Honolulu (HI), Chicago (IL), Boston (MA), Detroit (MI), Elizabeth
(NJ), JFK-New York, Jamaica (NY), Mayaguez, Ponce, San Juan (PR), Houston, Dallas
(TX), Seattle (WA), St. Croix, and St. Thomas (VI). Not all ports were represented in all
years.
From 1985 to April 2000, 7434 interceptions of Copitarsia were reported from 38 ports.
Only 3 records were found where specimens were identified to the species level. Without
adjusting for the volume or type of imported good/commodity, reported interceptions were
most common from Miami (Table 3). Reports from Miami were 7 times greater than the
10
Risk Assessment of Copitarsia spp.
second most commonly reporting port, Laredo, TX. Interceptions of Copitarsia from ports
along the U.S./Mexico border were greatest from Laredo and Hidalgo, TX. Notably,
Nogales, AZ and San Diego, CA, which receive a significant volume of agricultural
commodities from Mexico, reported few interceptions of Copitarsia (Table 3).
Among all interception records, Copitarsia were most commonly reported on permit cargo
(92.8%), baggage (3.2%), general cargo (2.7 %), and stores (1.1%). Mail, quarters, holds,
and miscellaneous collectively accounted for less than 1% of all interceptions. Port
specific information is provided in Table 3.
Copitarsia infested goods/commodities originated from at least 22 countries (Table 4).
Nearly twice as many reports were based on commodities from Colombia as compared to
Mexico. Colombia, Mexico, Ecuador, Guatemala, Chile, and Costa Rica accounted for
more than 95% of all interception records (Table 4).
Approximately 150 genera of plants were indicated in reports of Copitarsia interceptions.
Genera accounting for the top 90% of interceptions are reported in Table 5. The majority
(~60%) of reported interceptions were from ornamentals and cut flowers.
11
Table 3: Reported interceptions of Copitarsia spp. at various ports and pathways in which specimens were intercepted.
12
Port
Miami FL
Laredo TX
Hidalgo TX
San Juan PR
Dallas TX
Chile 2J
Brownsville TX
Houston TX
JFK Airport NY
Des Plaines IL
Los Angeles CA
Fort Lauderdale FL
New Orleans LA
Chicago IL
El Paso TX
San Antonio TX
Atlanta GA
Tampa FL
Roma TX
Elizabeth NJ
Pharr TX
Jacksonville FL
Port Arthur TX
Progresso TX
Nogales AZ
Savannah GA
Eagle Pass TX
Copitarsia
Interceptions
4617
656
589
252
247
191
172
171
118
62
46
45
43
38
34
34
19
18
14
13
9
8
8
5
4
4
3
Baggage
2.0
18.4
4.6
5.4
9.0
1.5
16.9
9.2
10.5
11.1
15.2
44.8
3.2
16.7
10.0
30.8
20.0
25.0
33.3
Mail
0.2
-
General
Cargo
1.9
2.0
1.7
0.6
9.4
5.9
56.0
89.5
6.7
2.9
3.2
22.2
15.4
-
% Observed in:
Permit
Cargo
Misc.
95.4
0.1
79.5
93.1
0.6
93.4
90.3
98.5
78.7
1.1
71.1
44.0
82.2
91.2
26.2
81.8
55.2
90.3
61.1
92.3
90.0
53.8
100.0
14.3
80.0
50.0
25.0
25.0
66.7
-
Stores
0.6
0.7
13.8
5.9
73.8
3.2
7.7
87.5
85.7
75.0
-
Quarters
0.2
3.0
12.5
-
Holds
0.2
-
Table 3 Continued:
13
Port
San Diego CA
Seattle WA
Argentina
Baton Rouge LA
Brooklyn NY
Corpus Christi TX
Del Rio TX
Galveston TX
Philadelphia PA
St. Louis MO
St. Paul MN
Total
Copitarsia
Interceptions
3
2
1
1
1
1
1
1
1
1
1
7434
Baggage
100.0
5.8
Mail
0.1
General
Cargo
50.0
100.0
5.0
% Observed in:
Permit
Cargo
Misc.
100.0
50.0
100.0
100.0
100.0
85.2
0.2
Stores
100.0
100.0
100.0
100.0
2.1
Quarters
0.1
Holds
0.1
USDA-APHIS
Table 4. Country of origin on reported interceptions of Copitarsia spp.
Commodity Origin
Colombia
Mexico
Ecuador
Guatemala
Chile
Costa Rica
Peru
Venezuela
Dominican Republic
Unknown
Honduras
Jamaica
Netherlands
Bolivia
Panama
Interceptions
4052 54.51%
2164 29.11%
524 7.05%
240 3.23%
195 2.62%
91 1.22%
74 1.00%
27 0.36%
10 0.13%
9 0.12%
8 0.11%
5 0.07%
5 0.07%
4 0.05%
4 0.05%
Commodity Origin
El Salvador
Venezuela (?)
Argentina
Brazil
Columbia (?)
Mexico (?)
Belize
Dominican Republic (?)
Guatemala (?)
Haiti
Japan
Nicaragua
St. Lucia
Trinidad and Tobago
Interceptions
3 0.04%
3 0.04%
2 0.03%
2 0.03%
2 0.03%
2 0.03%
1 0.01%
1 0.01%
1 0.01%
1 0.01%
1 0.01%
1 0.01%
1 0.01%
1 0.01%
Table 5: “Host” plants indicated in interception reports of Copitarsia spp.
Host
Limonium
Alstroemeria
Brassica
Dianthus
Coriandrum
Chrysanthemum
Pisum
Asparagus
Gladiolus
Chenopodium
Gypsophila
Aster
Lactuca
Unidentified Plant
Physalis
Rosa
Rosmarinus
Capsicum
Rubus
Helianthus
Common Name
Number Intercepted
Sea Lavender
Lily of the Incas
Cole Crops
Pinks/Carnation
Coriander/ Cilantro
Chrysanthemum
Pea
Asparagus
Gladiola
Quinoa
Baby’s breath
Aster
Lettuce
1998
1822
638
432
332
223
171
166
143
136
117
113
92
80
78
57
47
46
40
39
Husk tomato
Rose
Rosemary
Pepper
Blackberry
Sunflower
14
Percentage
Interceptions
26.88%
24.51%
8.58%
5.81%
4.47%
3.00%
2.30%
2.23%
1.92%
1.83%
1.57%
1.52%
1.24%
1.08%
1.05%
0.77%
0.63%
0.62%
0.54%
0.52%
Risk Assessment of Copitarsia spp.
Reported interceptions of Copitarsia show a strong seasonal pattern (Fig. 3). The majority
of insects are detected early in the year, with a peak occurring in March (P=0.0001), which
is in the fall in South America. Numbers are relatively low from July through September
(the South American winter), but tend to increase (though not significantly) in October.
The proportion of insects arriving from each country remained relatively constant
throughout the year. In June, July, and August of all years in the database, Copitarsia
were most frequently reported from Miami (751 interceptions), Laredo (110), and Hidalgo
(97). For the months of June, July, and August in Laredo, the majority of insects were
observed in June (46 interceptions) on Coriandrum sativum (22 interceptions). Over the
same time period in Hidalgo, the majority of insects were observed in July (also 46
interceptions) on Brassica spp. (21 interceptions).
1100
A
Average Ranking
Average Monthly Ranking
of Copitarsia Interceptions
10
Interception Number
AB
8
BC
900
BC
BC
1000
800
BCD
CD
6
700
CD
D
4
D
D
D
600
500
2
400
0
300
1
2
3
4
5
6
7
8
9
Total # of Copitarsia Interceptions
Figure 3: Monthly Dynamics of Copitarsia Interceptions. Months 1-12 refer to JanuaryDecember respectively. Bars with different letters are significantly different (P<0.05).
10 11 12
Month
Relatively few Copitarsia were intercepted during AQIM sampling. For the time period
we examined, 16 interceptions of Copitarsia were reported from 2,574 inspections of
border cargo of interest to USDA-APHIS-PPQ. The insect was intercepted on broccoli (7
records), Chinese cabbage (5), coriander (2) and lettuce (1). Cargo with Copitarsia
originated from Mexico, specifically Guanajuato (11 records), Tamaulipas (3), Districto
Federal (1), and Puebla (1). Infested shipments were originally destined for New York (8
records), Texas (7), and North Carolina (1). Eight interceptions occurred in Laredo: four
in Hidalgo and two from both Pharr and Brownsville. This pattern is generally consistent
with the trend observed from PIN-309 data. Copitarsia was intercepted on 0.8% of all
shipments of interest to PPQ (ranging from 2.56% at Laredo to 0% at most ports), but no
statistical differences in the percentage of interceptions could be detected between ports.
Only 1 interception of Copitarsia was reported from 1,681 inspections of regulated air
cargo. The insect was intercepted on a shipment of Gladiolus sp. from Mexico destined
15
USDA-APHIS
for Illinois. The small number of Copitarsia interceptions from AQIM samples
complicates the confirmation of trends.
Based on information from the PIN-309 database, cut flowers, ornamentals, and cole crops
(broccoli, cabbage, cauliflower) from Central and South America appear to be the most
significant pathways for the arrival of Copitarsia.
Quantity of Imports on Which Copitarsia Could Occur
The likelihood of arrival will be affected by trends in international trade. We speculate
that the probability of Copitarsia’s arrival into the U.S. will increase as agricultural
imports increase and diversify from countries with the pest. To examine recent patterns of
trade, we examined USDA-APHIS’ Plant Quarantine database (PQ-280). The database
was queried for all records of the importation of 19 Copitarsia host plants from 22
countries. The query included records from October 1993 to December 1999.
Straightforward comparison of trade records and Copitarsia interception reports was not
possible because the records came from different time periods. However, we assumed that
trade patterns from 1993-1999 were reflective of trade patterns from 1985 to 2000. To
relate trade and interception records, we ranked each country based on the total number of
shipments of Copitarsia hosts sent to the U.S. Each country was also ranked separately for
the number of Copitarsia interceptions. The two sets of ranks were then analyzed through
non-parametric regression.
The number of interceptions of Copitarsia from a particular country was related to the total
number of shipments of host plants imported from that country (R2=0.56, P<0.01). The
majority of edible host plants (i.e., fruits, vegetables, and herbs) were imported from
Mexico in terms of weight (metric tons) and total number of shipments received (Table 6).
Ornamentals and cut flowers arrived primarily from Colombia (2 billion stems in ~130,000
shipments) and Mexico (706 million stems in ~76,000 shipments). Imports from these two
countries accounted for 85% of the total number of stems imported from Western
Hemisphere countries. The greatest number of shipments of Copitarsia host plants (edible
and ornamental combined) came from Mexico, Colombia, the Netherlands, and Ecuador.
The vast majority of Copitarsia interceptions were reported on commodities from
Columbia, Mexico, and Ecuador, as might be expected given the large volume of trade in
commodities harboring Copitarsia. The number of interceptions of Copitarsia from a
country does not accurately reflect the level of risk that country might pose. For example,
certain commodities from Argentina may harbor Copitarsia, but since we currently do not
import those commodities from Argentina, our interceptions from that country are very
low. If we were to import more of these commodities from Argentina, we would expect to
intercept more Copitarsia.
16
Risk Assessment of Copitarsia spp.
Table 6: Origin of Copitarsia host plants imported into the US from Western Hemisphere
countries.
Origin
Argentina
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominican Republic
Ecuador
El Salvador
Guatemala
Guyana
Haiti
Honduras
Jamaica
Japan
Mexico
Netherlands
Nicaragua
Panama
Paraguay
Peru
St. Lucia
Trinidad and Tobago
Unknown
Uruguay
Venezuela
Grand Total
Fruits, Vegetables & Herbs1
Ornamentals & Cut Flowers2
Metric Tons
2,912
1,665
8
851
21,365
8,699
8,231
16,187
4,486
2
17,702
55
83
1,153
4,895
22
2,459,484
97,273
24
88
<1
66,322
2,511
2,280
Shipments
454
399
4
398
6,468
2,576
2,318
10,172
1,567
3
8,151
386
102
93
7,566
23
417,438
47,485
5
16
1
11,304
604
4,415
Stems (x1000)
<1
<1
2,645
970
523
2,069,661
62,153
50,230
247,950
1,036
81,201
<1
<1
<1
445
40
706,265
21,730
4
15,171
Shipments
6
5
1,240
18
327
127,691
6,414
2,981
46,283
31
8,789
1
25
1
31
29
76,107
20,364
1
272
<1
49
2,716,345
1
12
521,961
8,226
<1
46
36
<1
35
3,268,366
1,589
1
45
4
1
61
292,317
Total Shipments
460
404
1,244
416
6,795
130,267
8,732
13,153
47,850
34
16,940
387
127
94
7,597
52
493,545
67,849
6
288
1
12,893
605
4,460
4
2
73
814,278
1 Includes plants within the genus Brassica, Capsicum, Chenopodium, Coriandrum,
Lactuca, Physalis, Pisum, Rosmarinus, and Rubus.
2 Includes plants within the genus Alstroemeria, Aster, Chrysanthemum, Dianthus,
Gladiolus, Gypsophila, Helianthus, Limonium, and Rosa.
Differences in the number of shipments of various commodities imported to the U.S. did
not account for differences in the number of Copitarsia intercepted on these commodities
(R2=0.04, P>0.1). The volume of the import (metric tons or stems) also did not explain
variation in the number of interceptions (Table 7). Capsicum spp. (i.e., peppers) were most
frequently shipped to the U.S. but were ranked 17 out of 19 in terms of the number of
Copitarsia intercepted. Copitarsia was intercepted most commonly on Limonium spp., but
this commodity was ranked 7 (of 19) based on the total number of shipments received.
Host plant is an important predictor of risk. Host plants differ in quality as a host for
Copitarsia, pretreatment options to control the pest, mode of transportation (e.g., air vs.
maritime cargo), and methods by which they are sampled. Future research will be needed
to explain why Copitarsia tends to be reported more frequently from certain commodities,
17
USDA-APHIS
but for the moment, we feel confident that differences are not due simply to the number of
shipments received.
Table 7: Quantity of selected Copitarsia hosts imported into the US from Western
Hemisphere countries.
Fruits, Vegetables & Herbs
Ornamentals & Cut Flowers
Host Metric Tons Shipments
Host Stems (x1000) Shipments
Asparagus
269,872
42,014
Alstroemeria
146,382
23,448
Brassica
315,901
50,035
Aster
17,510
19,033
Capsicum
1,696,803
306,820 Chrysanthemum
499,567
38,640
Chenopodium
55
63
Dianthus
1,155,029
56,777
Coriandrum
105,712
34,997
Gladiolus
62,338
12,069
Lactuca
87,600
22,162
Gypsophila
192,894
26,045
Physalis
222,803
47,901
Helianthus
11,058
5,884
Pisum
364
189
Limonium
226,158
37,883
Rosmarinus
697
5,152
Rosa
957,430
72,538
Rubus
16,538
12,628
Grand Total
2,716,345
521,961
Grand Total
3,268,366
292,317
Differences in the number of shipments arriving at different U.S. ports also did not account
for differences in the number of Copitarsia intercepted at each port (Table 8). Copitarsia
host plants from Western Hemisphere countries most commonly arrived at Miami
(192,000 shipments), San Diego (144,000), and Nogales (130,000). No Copitarsia were
intercepted from San Diego, and only 4 specimens were reported from Nogales. As a
result, we conclude that different ports are likely to have different levels of risk of
receiving Copitarsia that is independent of the number of shipments that are imported.
Among countries in the Western Hemisphere, the volume of certain host plants (e.g.,
Limonium and Alstroemeria) imported at each port that is probably the most important
predictor of risk.
18
Table 8: Total shipments of Copitarsia host plants arriving at U.S. ports from Western Hemisphere countries.
19
Port
Anchorage
Atlanta
Baltimore
Bangor
Blaine
Boston
Brooklyn
Brownsville
Buffalo
Calexico
Charleston
Charlotte
Chicago
Columbus
Columbus-NM
Dallas
Dayton
Del Rio
Denver
Shipments
2,620
8,288
586
3
240
4,469
3
5,813
38
35,426
23
1
86
94
10,185
12,719
29
1
397
Port
Detroit
Douglas
Dulles
Eagle Pass
El Paso
Elizabeth
Erlanger
Fort Lauderdale
Galveston
Hidalgo
Honolulu
Houston
Jacksonville
JFK
Laredo
LAX
Long Beach
Los Angeles
Mayguez
Shipments
123
186
11,292
676
36,806
3,157
850
262
1
11,908
38
12,275
5
26,466
15,690
24,299
196
3,572
5
Port
Memphis
Miami
Minneapolis
Nashville
New Orleans
Nogales
Oakland
O'Hare
Orlando
Pharr
Philadelphia
Phoenix
Pittsburgh
Ponce
Port Hueneme
Presidio
Progresso
Raleigh
Rio Grande City
Shipments
2,931
192,801
1,563
701
323
130,214
13
2,628
1,676
28,061
1,479
119
6
1
11
469
77
32
3,250
Port
Roma
Rouses Point
San Antonio
San Diego
San Francisco
San Jose
San Juan
San Luis
Santa Fe
Santa Rosa
Seattle
St. Croix
St. Thomas
Tampa
Tucson
Vermont
West Palm Beach
Wilmington
Grand Total
Shipments
8,821
44
1,145
144,274
5,890
263
21,869
32,302
1,868
302
1,261
278
53
457
145
1
160
13
814,329
USDA-APHIS
Collectively, these analyses reveal that the risk of Copitarsia arriving at an individual port
cannot be assessed simply on the basis of the total volume or the total number of shipments
of host plants that a port receives. Pretreatment of commodities and Copitarsia’s
preference for certain hosts are likely to influence the risk of Copitarsia arriving at a
particular port. Current information sources do not reliably indicate the pest’s association
with a particular commodity. Association inferred from interception records could be the
result of different sampling strategies applied to each commodity at the port of entry.
Clearly, identifying the actual pest load on particular commodities is another important
area of research.
Probability that Copitarsia will not be Detected at the Port of Entry
Two issues affect the ability of a PPQ officer to detect a pest at a border port. The first
issue is whether or not the pest organism is easy or difficult to detect during routine
inspections. Copitarsia larvae are external feeders, approximately ¾ to 1½ inch in length
when mature. They are generally found individually on fresh vegetables, flowers, and
herbs. The larvae are relatively easy to detect by the quantities of frass found on the host.
Adults are generally not encountered during the inspection of agricultural cargo. At this
time, no traps or lures are available specifically for detecting this pest. Copitarsia larvae
encountered at Ports of Entry on the Mexican border can be confused with larvae of the
noctuids, Spodoptera and Peridroma. Identifiers have assembled keys and photographs
(Appendix I) that are used as training aids for inspectors, who feel confident in their ability
to distinguish genera when properly trained. In summary, PPQ officers do not consider it
difficult to locate or identify Copitarsia larvae in boxes of produce that they inspect.
The second issue is whether or not a sufficient number of boxes per shipment are being
inspected to detect Copitarsia. Currently, cargo that enters the U.S. may be inspected
following two sampling strategies. The first is recommended under Agricultural
Quarantine Inspection Monitoring (AQIM) and is often referred to as hypergeometric
random sampling. The term ‘hypergeometric’ refers to a special statistical distribution that
describes the probability of detecting 1, 2, or more larvae (considered successes) after
inspecting a certain number of boxes, when the total number of larvae in the shipment is
known (or is assumed) and the total shipment size is relatively small. The sampling
strategy recommended under AQIM is designed to detect pests (assuming a 10%
infestation level) with 95% confidence. Less than 1% of all shipments entering the US are
currently inspected following AQIM guidelines. Alternatively, cargo may be inspected
following a 2% rule-of-thumb (i.e., 2% of the shipment is inspected for the presence of
pests). The percentage may increase or decrease depending on the size of the shipment,
the perceived level of risk, and on the total number of shipments that an officer must
inspect. In general, as inspection demands increase, the sample size decreases. Ideally, the
2% sample should be drawn at random from a shipment, but tailgate inspections (i.e.,
inspection of boxes that are accessible at the end of a truck) remain common.
We conducted an analysis to determine the probability of releasing cargo for entry into the
U.S. when AQIM guidelines or the 2% rule was followed. We assume that all shipments
are 10% infested. Because the number of samples collected under the 2% rule is small
relative to the shipment size, we apply binomial statistics to this scenario. From binomial
statistics, it can be shown that the probability of observing 1 or more larvae is P[X1]=1(1-p)N. In this analysis, the proportion of infested cargo, p = 0.10 (i.e. 10% infestation).
20
Risk Assessment of Copitarsia spp.
We conducted a further analysis to look at the infestation rate needed to detect 1 larva
with 95% confidence. If the probability of detection, P[X1], is set at 95% (=0.95) and the
number of samples, N, is known (e.g., 2% of the shipment), we can rearrange the equation
to solve for p. In this case, p is the level of infestation that would be necessary in order to
detect a pest with 95% confidence when 2% of the shipment is sampled.
Samples collected following AQIM guidelines maintain a 95% probability of detecting
pests at an infestation level of 10%, regardless of the size of the shipment (Table 9). Under
these guidelines, the proportion of the shipment that is inspected is greater when the
shipment is small. The total number of samples to be collected quickly approaches 30 as
the shipment size becomes greater. When a constant 2% of the shipment is sampled, pests
in small shipments are not likely to be detected. The goal of detecting pests with 95%
confidence is only achieved when the shipment size is large (i.e., 1,500 boxes) and 30
boxes are inspected (Table 9). As the shipment size (and the number of boxes inspected)
declines, the level of infestation must be very high to be 95% certain that pests will be
detected. For example, in a shipment of 100 boxes where 2 boxes are inspected, there is a
5% chance that a port officer will select 2 “clean” boxes simply by chance even when 78%
of the boxes are infested. As the infestation level declines, the chances of selecting an
infested boxes also declines (data not shown). When the 2% rule is followed in shipments
smaller than 500 boxes, PPQ officers are not likely to detect Copitarsia with 95%
confidence unless >25% of the commodity is infested (Table 9). Because AQIM sampling
is not widely used, the overall likelihood that Copitarsia has entered the U.S. in
commodity shipments is quite high.
21
USDA-APHIS
Table 9: Sampling requirements and corresponding probability of detecting Copitarsia
when inspecting shipment s following AQIM protocols or the 2% rule.
Boxes on Truck
10
50
100
200
300
400
500
1000
1500
1
AQIM
Probability of
Boxes to
detecting
Inspect
infested cargo1
10
100%
22
95.4%
25
95.2%
27
95.3
28
95.5%
28
95.3%
28
95.2%
29
95.5%
29
95.4%
Boxes to
Inspect
1
1
2
4
6
8
10
20
30
2% Rule
Probability of Infestation needed to
detecting
detect 1 larva with
infested cargo1
95% confidence
10.0%
95%
10.0%
95%
19.0%
78%
34.4%
53%
46.9%
39%
57.0%
31%
65.1%
26%
87.8%
14%
95.8%
10%
Assumes 10% of the shipment is infested with larvae when it arrives at a port.
These analyses assume that boxes were loaded and samples were selected at random.
Intentional placement of infested boxes in less accessible areas of the shipment will lower
the probability of detecting infested shipments.
Likelihood of Natural Dispersal into the United States
We could find few references about the method or distance of dispersal of any of the
Copitarsia species. Small larvae of C. consueta are reported to produce silk threads and
are thought to be carried great distances by the wind (Artigas and Angulo 1973b). These
“great distances” were not reported. Larvae of the common armyworm, Mythimna
convecta, also disperse on silk threads; about 30% of these larvae were transported 2m
after 7 days of light to medium breezes (McDonald 1991). Although a small proportion of
Copitarsia larvae could be transported greater distances under the right environmental
conditions, larval dispersal is unlikely to allow populations to spread far or rapidly.
Some noctuid species migrate as adults over immense distances. Examples include
Spodoptera frugiperda (over 900 miles in 30 hours; Johnson et al. 1987), Spodoptera
exempta (up to 56 miles in one night and 91 miles total; Rose et al. 1985), Heliothis
virescens (as much as 70 miles in 4-5 days; Hendricks et al. 1973), Helicoverpa zea (as far
as 409 miles; Westbrook et al. 1997), and Agrotis ipsilon (up to 785 miles; Showers et al.
1989). Many of these species actively move high above the ground to encounter strong
winds that aid dispersal (Riley et al. 1983), and moving warm and cold weather fronts and
strong winds associated with hurricane periods can aid adult dispersal (Sparks 1979).
Our current understanding of the distribution of Copitarsia in Mexico is that it occurs
south of Ciudad Victoria, Mexico. Ciudad Victoria, Mexico is 179 miles from McAllen,
TX. If Copitarsia exhibit migratory flights and winds flow northward from southern
Mexico to south Texas, the probability of natural dispersal of Copitarsia into Texas is
high. If the conditions necessary for long distance dispersal are not met, then the
probability of Copitarsia naturally entering the United States is low. Determining the
dispersal capabilities of Copitarsia is an important area for future research.
22
Risk Assessment of Copitarsia spp.
Copitarsia may also be capable of dispersing from countries other than Mexico. Although
there is no literature confirmation of establishment of Copitarsia in the Caribbean, APHIS
has intercepted Copitarsia in shipments of produce from the Dominican Republic,
Jamaica, Haiti, St. Lucia, and Trinidad and Tobago. In fact, more Copitarsia were found
in shipments from the Dominican Republic and Jamaica than from Argentina, where
Copitarsia is known to occur. Montego Bay, Jamaica is 511 miles from Homestead, FL.
Cuba is only 114 miles from Jamaica, and 200 miles from Florida. Two other noctuid
species, Heliothis virescens and Heliothis zea, have been documented to island hop in the
Caribbean, having moved from St. Croix to Vieques (65 miles) and St. Thomas (45 miles).
Copitarsia species may be established in the Caribbean, and depending on their propensity
for long-distance migration, they could invade Florida naturally.
Commodity Treatments that Could Reduce the Risk of Invasion
The risk of Copitarsia entering the U.S. could be reduced if commodities were treated
prior to entry. Other commodities, such as papaya and mango, are treated for the
Mediterranean fruit fly before they are allowed into the United States. The APHIS
commodity treatment manual lists treatments for commodities found to have Copitarsia.
For asparagus, banana, blackberry, cole crops, melons, plantain, and raspberries the
recommended treatment is methyl bromide. Methyl bromide will only be available for a
few more years because of federal regulations. The only other recommended treatment
was the use of a malathion-carbaryl chemical dip for chrysanthemums. Consequently, few
post-harvest treatments, if any, will be available to reduce the number and risk of
Copitarsia arriving in the U.S..
23
USDA-APHIS
PROBABILITY OF ESTABLISHMENT IN THE UNITED STATES
Establishment of an exotic species occurs when immigrants begin to reproduce in their
new environment (Venette and Carey 1998). Several factors contribute to the successful
establishment of species, including the appropriateness of the climate, the availability of
sustenance, the number of individuals in a founding population, and the activity of
antagonists (e.g., generalist predators, pathogens, parasitoids, or competitors). The relative
importance of these factors has been the subject of debate among population and
community ecologists for decades (e.g., Pimm 1991, Davis et al. 1998). Both biotic and
abiotic forces have a significant impact on population dynamics.
Potential Range in USA based on Climatic Tolerance
For ectothermic organisms, climate is a fundamental determinant of invasion success. In
particular, temperature and moisture conditions influence population dynamics of a species
in two basic ways. First, climate affects the rate at which individuals reach reproductive
maturity and populations begin to grow. In general, developmental and growth rates
increase as temperatures rise above some lower threshold (commonly ~10°C).
Consequently, development is often described in terms of “degree days.” If too few degree
days are acquired, ectothermic organisms will fail to mature and reproduce. Second, more
extreme environmental conditions which are not suitable for population growth (e.g., too
wet, dry, cold or hot) may eliminate a population. To establish, a newly invading species
must be able to withstand these stressful conditions. Native organisms have evolved
numerous physiological and behavioral mechanisms to persist during adverse
climatological conditions. Some of these adaptations (e.g., diapause) may also allow an
invading species to survive in a new habitat, assuming the habitat also provides the
necessary cues to trigger an appropriate survival response. However, the degree of
survivorship during these adverse conditions will depend on the preparedness of the
invading population and the severity (both in terms of length and degree) of the adverse
condition.
The climatological software Climex (CSIRO, Australia; Sutherst and Maywald 1985) has
been used to identify geographic areas where population growth and persistence of an
invading species is likely (e.g., Worner 1988, Sutherst et al. 1989, Allen et al. 1993,
Sutherst et al. 1996, Venette and Hutchison 1999). Climex contains a database with longterm climatological records for ~2,500 cities worldwide. The database includes monthlyaverage maximum air temperature, minimum air temperature, precipitation, and relative
humidity (measured in the morning and afternoon). The information within the database
can be used in two basic ways to determine which areas of the world are likely to provide
suitable habitat: climate matching or population modeling (Venette and Hutchison 1999).
The objective of this section of the pest risk assessment was to identify areas within North
and South America that are likely to support the establishment of Copitarsia, to quantify
the probability of establishment in these areas, and to determine which climatic parameters
are most predictive of current and future establishment. This same set of analyses can be
used to determine the potential range of Copitarsia within the U.S. We focus on the genus
as a whole rather than dealing with individual species because the taxonomy within the
genus Copitarsia is poor. The sparse information on the geographic distribution or
24
Risk Assessment of Copitarsia spp.
ecology of Copitarsia that could be gathered from the literature could not be assigned
reliably to any one species.
Two independent approaches using Climex, following methods described in Venette and
Hutchison (1999), were used to identify regions with climatically suitable habitat for
Copitarsia. The first approach was based on matching the climate of locations where
Copitarsia is known to occur to the remainder of North and South America. We presume
that areas with a greater degree of climatic similarity to a Copitarsia-infested site are more
likely to support establishment of the insect than areas which are less similar. The second
approach was based on modeling the effect of climate on population dynamics of
Copitarsia. The potential impacts of climate were inferred from the known geographic
distribution of the species. Methodological details and statistical analyses for each method
are described in Appendix IV.
Detailed results are presented in Appendix V. Within the United States, California and
Oregon were most climatically similar to Copitarsia-infested sites in South America (Fig.
4). However, the degree of similarity depended on which climatic parameters were
examined. For example, the entire west coast was 70% similar to a Copitarsia-infested
site based on minimum air temperatures (Fig. 4, A). In terms of total precipitation, nearly
the entire US was 90% similar to a Copitarsia-infested site (Fig. 4, B). Overall climatic
similarity based on temperature and moisture combined was 70% in coastal California
and Oregon (Fig. 4, C).
25
USDA-APHIS
Figure 4: Maximum climatic similarity between any 1 of 12 “target” locations with
established populations of Copitarsia spp. and the Western Hemisphere based on: A)
minimum temperatures; B) precipitation; and C) the Match Index, a measure of overall
climatic similarity. Diamonds indicate “target” locations.
Interpretation of climatic similarity measurements can be complicated. Specifically, how
similar does climate need to be before establishment of Copitarsia should be considered
likely? To answer this question, regions must be identified where establishment can be
judged unlikely. Based on the known distribution of Copitarsia in South America, we
speculate that the insect cannot withstand extended periods of freezing temperatures (i.e.,
2 months at 0°C). When we compare locations that satisfy this criterion with locations
in South America that have Copitarsia, we find that that locations with Copitarsia were
significantly different from our “Copitarsia-free” areas in terms of temperature but not in
terms of precipitation (Table 18 in Appendix V). Logistic regression indicated that
similarity measures based on minimum temperature and total precipitation best predicted
the probability of Copitarsia establishment.
Based on measures of climatic similarity, the probability of Copitarsia establishment in the
U.S. was 90% in California, Oregon, and Washington (Fig. 5). Pockets existed in Idaho,
Nevada, New Mexico, Texas, Oklahoma, Arkansas, Missouri, Indiana, Illinois, Kentucky,
Tennessee, Georgia, South Carolina, North Carolina, Virginia, West Virginia, Maryland,
Pennsylvania, New Jersey, and Florida where the probability of establishment was 50%.
We recognize that climate matching may not accurately represent the true risk of
Copitarsia establishment in certain parts of the U.S. The climate of South America can
vary considerably over short distances due to changes in elevation (Gasith and Resh 1999).
Our approach for selecting “target” cities for climate comparisons was very conservative.
From the list of cities for which we had climatic information, we selected only those
locations that were well within the known distribution of the genus.
26
Risk Assessment of Copitarsia spp.
Figure 5: Probability of Copitarsia establishment in the Western Hemisphere based on
logistic regression analysis of climate similarity indices.
Modeling the impact of climate on the population dynamics of Copitarsia provides an
independent assessment of the likelihood of establishment in the U.S. Models suggest that
Copitarsia could accrue 4,500 - 9,100 degree days (base 4°C) between ~40°S latitude and
~35°N latitude and 2,250 - 4,500 degree days in much of the remainder of the U.S. (Fig. 6,
A). [From data presented in Arce de Hamity and Neder de Roman (1992), and Larrain-S.
(1996) we estimate that Copitarsia requires 834 - 1,320 degree days to complete a
generation.] Cold stress was greatest in Canada and the northern U.S. (Fig. 6, B). Values
for an Ecoclimatic Index, an integral measure of a species’ growth potential and ability to
withstand environmental stresses, were 10% in Washington, Oregon, California, and most
states east of the Rocky Mountains (Fig. 6, C). Interpretation of output from Climex’s
biological models can also be complex. Logistic regression suggested that an index of cold
stress, alone, provided the best indication of the probability of establishment.
27
USDA-APHIS
Figure 6: Indices of potential Copitarsia growth and stress in the Western Hemisphere
based on biological parameters inferred from the geographic distribution of the genus
within Chile. A) Degree days; B) Cold stress; and C) Ecoclimatic index, an integral
measure of an organism’s ability to persist and grow under average climatic conditions.
In the US, biological models within Climex suggest that the probability of Copitarsia
establishment is 90% in much of the southeastern US, California, Oregon, and
Washington (Fig. 7). The probability of establishment is 50% in 36 states. Establishment
is unlikely in Alaska, Montana, Wyoming, Utah, Colorado, Nebraska, South Dakota, North
Dakota, Minnesota, Iowa, Wisconsin, Vermont, New Hampshire, and Maine.
28
Risk Assessment of Copitarsia spp.
Figure 7: Probability of Copitarsia establishment in the Western Hemisphere based on
logistic regression analysis of Climex indices of stress and population growth.
Which of the approaches gives the right answer? In the absence of additional data, it is
impossible to say which model is more correct. Quantitative studies on the effects of
climate on Copitarsia population dynamics are scarce (but see Arce de Hamity and Neder
de Roman 1993, Larrain-S. 1996). Understandably, most published reports examine
temperature or moisture conditions where Copitarsia currently exists, not where it might
exist in the future. As a result, we relied on parameter estimates generated through
iterative “geographic fitting” (Sutherst and Maywald 1985) to provide the best estimate of
the impact of climate on Copitarsia population dynamics. These estimates should be
considered working hypotheses that remain to be formally tested through controlled
experiments. In particular, the impact of cold stress and wet soil on Copitarsia mortality
appear to be critical knowledge gaps.
29
USDA-APHIS
Both climate matching and biological models indicate that concern over Copitarsia is
warranted. Both approaches predict 90% probability of establishment in California,
Oregon, and Washington. Both analyses also suggest that the probability of establishment
is between 50 and 70% in western Idaho and is <10% in the Upper Midwest. In other
regions, the two approaches produce slightly different probability estimates. Consistently
in this study, climate matching leads to a slightly lesser estimate and biological modeling
leads to a greater estimate of the probability of establishment. Nevertheless, both models
agree that establishment is likely (i.e., the probability is >50%) in the Southwest
(specifically, New Mexico), the southern Plains states (Texas and Oklahoma), the
Southeast (Arkansas, Missouri, Kentucky, Tennessee, Georgia, South Carolina, North
Carolina, Virginia, West Virginia, Maryland, and Florida), parts of the mid-Atlantic states
(Pennsylvania and New Jersey), and the Great Lakes region (Indiana and Illinois).
Our analyses make several important assumptions. First, we assume that adequate host
plants are available. This assumption seems valid as Copitarsia has a broad host range and
can feed on members of at least 19 plant families. Second, we assume that climate is the
only factor that constrains the distribution of the species. Interactions with antagonists
may further limit the probability of establishment (Davis et al. 1998), but interactions may
be innumerable and may not always lead to a predictable outcome. Additionally, we did
not attempt to account for the effect of current cultural practices on the probability of
establishment. Applications of insecticides, production of transgenic insecticidal crops,
selection of resistant varieties, and alteration of planting dates designed to control resident
lepidopteran pests in the U.S. may reduce Copitarsia’s chances of becoming established.
Finally, this analysis assumes that arrival of Copitarsia into any part of the U.S. is equally
likely.
Results from our analyses should be interpreted with caution. In this assessment, we have
focused on establishment, which we define as the maintenance of a population though
local reproduction without reliance on continual immigration (Venette and Carey 1998).
The probability of establishment is evaluated independently of the probability of arrival
into different regions into the U.S. We consider arrival a fundamentally different aspect of
the invasion process. Furthermore, establishment of Copitarsia does not imply that
outbreaks will occur or that host plants will be significantly damaged. Conceivably,
populations could become established but remain at low levels, perhaps even below the
level of detection, for years (Carey 1991). Alternatively, our analyses may significantly
underestimate the area that could be affected by Copitarsia. Other noctuids in the U.S. can
be major pests in areas where they cannot overwinter because of their capacity for long
distance dispersal. For example, Helicoverpa zea regularly disperses into the northern half
of the country from the South. Although H. zea cannot overwinter in northern states, the
insect is still responsible for significant economic losses in these areas. Similarly
Copitarsia may be capable of migrating from areas where it is permanently established to
adversely affect other areas of the country.
Host Plant Range
For a phytophagous organism to establish in a foreign country, it must be able to find and
utilize appropriate host plants. It is likely that both crop and non-crop native plants could
serve as host plants for Copitarsia in the United States.
30
Risk Assessment of Copitarsia spp.
Crop Plants
Moths in the genus Copitarsia are considered polyphagous, and indeed there are records of
them feeding on many host plants. We gathered host plant records from a variety of
sources. The first was the taxonomic literature, including Artigas and Angulo (1973a) and
Castillo and Angulo (1991), where host plant information was provided for different
species of Copitarsia. Several other manuscripts present anecdotal evidence of host plant
associations. Finally, there are references of Copitarsia found at U.S. border crossings on
several hosts that are not mentioned in the literature. Because of the difficult taxonomy of
this genus (see Appendix II) we will present host plant information for the genus
Copitarsia and not for individual species.
Thirty-nine crop plants are listed as hosts for Copitarsia in the published literature, and
Copitarsia have been found on more than nine additional crops at U.S. ports of entry
(Table 10). These crop plants represent 19 different families. Most of the crops listed as
hosts of Copitarsia are grown in the United States. We analyzed data from the USDANational Agricultural Statistics Service (USDA-NASS 1998) and the Texas Agricultural
Statistics Service (Texas Agricultural Statistics Service 1999) to determine the extent of
production of host crop plants in the U.S. Figure 8 shows that plants that support
Copitarsia larvae are grown in almost every county in the continental United States. The
distribution of crop production for individual crops is provided in Appendix VI.
The predicted distribution of Copitarsia in the U.S. is closely matches the distribution of
certain host crops, especially artichoke, asparagus, beets, blackberry, broccoli, brussel
sprouts, cabbage, cauliflower, garlic, lettuce, onion, pea, raspberry, spinach. Both
CLIMEX models predict a low probability of Copitarsia establishment in the central
United States, where the majority of crops grown are corn, wheat, sunflower, and flax.
Although these plants have been reported as hosts, there is no strong evidence that these
plants are favored by Copitarsia in South America. We therefore removed these common
crops from the analysis to examine regional trends in the production of specialty crops
(Fig. 9). CA, OR, WA, south FL, NC, and KY have the highest acreage of Copitarsia
crop plants (red and yellow) and are states where CLIMEX predicts the environment is
most suitable for establishment of Copitarsia. This overlap may not be a coincidence. It is
likely that the environments in both South and North America that are conducive for
production of the types of plants on which Copitarsia feeds are favorable for the
development of Copitarsia populations on both continents.
With the exception of wheat and corn, Texas and Florida do not extensively produce crops
that are potential hosts for Copitarsia. The fact that the pest is intercepted most often in
TX and FL, where the incidence of favored host plant crops is lowest, might explain why
Copitarsia has not yet established in the U.S.
31
Table 10: Plants that are hosts for Copitarsia species larvae.
English
Common Name
Alfalfa
Spanish
Common
Name
Alfalfa
Apple
Artichoke
Manzana
Alcachofa
Malus sp.
Cynara
scolymus
Rosaceae
Asteraceae
Chile
Chile
Asparagus
Espárragos
Asparagus
officinalis
Aster spp.
Gypsophila
spp.
Beta vulgaris
Liliaceae
Chile
Asteraceae
Caryophyllaceae
Aster1
Baby’s-breath1
References
Scientific
Name
Medicago
saliva
Countries
Family
Fabaceae
32
Beet
Remolacha,
betabel
Bell pepper1
Pimiento
verde
Habas
Capsicum sp.
Solanaceae
Vicia faba
Fabaceae
Broccoli
Broccoli
Brassicaceae
Cabbage
Repollo
Carnation
Clavel
Carrot
Zanahoria
Brassica
oleraceae
Brassica
oleracea
Dianthus
caryophyllus
Daucus
carota subsp.
Siuivus
Broad or Lima
beans
Chenopodiaceae
Brassicaceae
Chile,
Mexico,
Peru
PIN-309
PIN-309
Chile,
Mexico,
Argentina
Mexico
(Castillo and Angulo 1991; Larrain-S. 1996; Rojas et al. 1993; Arce de
Hamity and Neder de Roman 1992; Angulo and Weigert 1975b; Neder de
Roman and Arce de Hamity 1991)
PIN-309 and (Riley 1998a)
Chile,
Peru,
Argentina
Mexico
(Lamborot et al. 1995; Gomez-T. 1972), (Arce de Hamity and Neder de
Roman 1992; Neder de Roman and Arce de Hamity 1991)
Chile,
Mexico
(Carrillo-S. 1971; Monge-V. et al. 1984; Aruta-M et al. 1974; Larrain-S.
1996; Rojas et al. 1993; Castrejon-G. et al. 1998; Grez 1992)
(Castillo and Angulo 1991)
Caryophyllaceae
Apiaceae
(Castillo and Angulo 1991; Cortes 1976; Porter 1980; Hichins-O. and
Rabinovich 1974; Hichins-O. and Mendoza-M. 1976; Arce de Hamity and
Neder de Roman 1993; Larrain-S. 1996; Apablaza and Stevenson 1995;
Rojas et al. 1993; Arce de Hamity and Neder de Roman 1992; Angulo and
Weigert 1975b)
(Larrain-S. 1996)
(Castillo and Angulo 1991; Machuca et al. 1990; Machuca et al. 1989b;
Machuca-L. et al. 1988; Larrain-S. 1996; Machuca et al. 1989a; Larrain-S.
1984; Larrain-S. and Araya-C. 1994)
(Castillo and Angulo 1991; Larrain-S. 1996)
Argentina
(Rojas et al. 1993; Castrejon-G. et al. 1998)
(Arce de Hamity and Neder de Roman 1992; Neder de Roman and Arce de
Hamity 1991)
English
Common Name
33
Cauliflower
Spanish
Common
Name
Coliflor
Chard
Acelga
Chick pea
Garbanzo
Clover
Trébol
Coriander1
Cilantro
Corn
Eggplant
Maíz
Berenjena
Field
smartweed
Flax or linen
Malezas
Garlic
Ajo
Gladiolus1
Grape
Galdiolo
Uva, vid
Lino
Groundcherry1
Husk tomato1
Jojoba
Jojoba
Kiwi
Kiwi
Lettuce
Lechuga
References
Scientific
Name
Brassica
oleracea
botrytis
Beta vulgaris
ssp. cicla
Cicer
arietinum
Trifolium
pratense
Coriandrum
sativum
Zea mays
Solanum
melongena
Polygonum
segetum
Linum
usitatissimum
Allium
sativum
Gladiolus spp.
Vitis spp.,
Vitis vinifera
Physalis spp.
Physalis
pubescens
Simmondsia
californica
Actinidia
chinensis
Lactuca spp.
Countries
Family
Brassicaceae
Chenopodiaceae
Fabaceae
Mexico
(Castrejon-G. et al. 1998; Rojas et al. 1993)
Mexico,
Chile
Chile
(Rojas et al. 1993; Lamborot et al. 1995)
Fabaceae
(Larrain-S. 1996)
(Castillo and Angulo 1991)
Apiaceae
Mexico
Poaceae
Solanaceae
Chile
Chile
Polygonaceae
Colombia
(Castillo and Angulo 1991; Zenner de Polenia 1990)
Linaceae
(Castillo and Angulo 1991; Angulo and Weigert 1975b; Wille-T. 1943)
Iridaceae
Iridaceae
Chile,
Peru
Colombia,
Chile
Mexico
Chile
Solanaceae
Solanaceae
Mexico
Buxaceae
Chile
(Castillo and Angulo 1991; Quiroga et al. 1989; Larrain-S. 1996)
Actinidiaceae
Chile
(Larrain-S. 1996)
Asteraceae
Argentina,
Mexico
Liliaceae
(Riley 1998a)
(Castillo and Angulo 1991; Larrain-S. 1996; Olivares and Angulo 1995)
(Lamborot et al. 1995)
(Lopez-A. 1996b; Larrain-S. 1996)
PIN-309 and (Riley 1998a)
(Castillo and Angulo 1991; Larrain-S. 1996)
PIN-309
(Riley 1998a)
(Arce de Hamity and Neder de Roman 1992; Neder de Roman and Arce de
Hamity 1991)
English
Common Name
Spanish
Common
Name
Lily of the
Incas1
Marigold
Maravilla
Onion
Cebolla
Peas
Potato
Arverjas,
chicaro
Pistacho or
pistacio
Papa
Quinoa or
quinua
Rapeseed
Quinoa or
quinua
Raps
Raspberry
Rose1
Rosemary
Frambuesa
Ryegrass
Ballica
Pistacio
References
Scientific
Name
Alstroemeria
spp.
Calendula
spp.
Allium cepa
Countries
Family
Liliaceae
Asteraceae
Chile
Liliaceae
Chile,
Mexico,
Colombia
Chile,
Mexico
Chile
(Castillo and Angulo 1991; Lamborot et al. 1995; Larrain-S. 1996; LopezA. 1996a; Quiroz-E. 1977; Castrejon-G. et al. 1998)
Mexico,
Colombia,
Bolivia,
Chile,
Argentina,
Peru
Chile,
Bolivia
Chile
(Castillo and Angulo 1991; Loo-P and Aguilera 1983; Arestegui-P. 1976;
Sanchez-V. and Maita-Franco 1987; Leyva-O. and Sanchez-V. 1993;
Larrain-S. 1996; Lopez-A. 1996a; Rojas et al. 1993; Arce de Hamity and
Neder de Roman 1992; Munro 1968; Angulo and Weigert 1975b; Olivares
and Angulo 1995; Zenner de Polenia 1990)
Pisum spp.
Fabaceae
Pistacia spp.
Anacardiaceae
Solanum
tuberosum
Solanaceae
Chenopodium
quinoa
Brassica
napus
Rubus idaeus
Rosa spp.
Rosmarinus
officinalis
Lolium
multiflorium
Limonium
spp.
Spinacia
oleracea
Fragaria
chiloensis
Chenopodiaceae
34
Romerito
Sea lavender1
Spinach
Espinaca
Strawberry
Frutilla
PIN-309
Brassicaceae
Rosaceae
Rosaceae
Lamiaceae
Chile
Mexico
Lamiaceae
Chile
Plumbaginaceae
(Larrain-S. 1996)
(Lamborot et al. 1995; Rojas et al. 1993)
(Larrain-S. 1996)
(Castillo and Angulo 1991; Lamborot et al. 1999; Liberman-Cruz 1986;
Angulo and Weigert 1975b)
(Artigas and Angulo 1973a; Castillo and Angulo 1991; Larrain-S. 1996)
(Larrain-S. 1996; Castillo and Angulo 1991)
PIN-309
(Rojas et al. 1993)
(Angulo and Weigert 1975b; Castillo and Angulo 1991)
PIN-309
Chenopodiaceae
Mexico
(Rojas et al. 1993; Castillo and Angulo 1991)
Rosaceae
Chile
(Larrain-S. 1996; Castillo and Angulo 1991)
English
Common Name
Sunflowers
Spanish
Common
Name
Girasol
Tobacco
Tobacco
Tomato
Tomate
Ulluco
Melloco,
olluco,
ulluma,
chuguas
Trigo
Wheat
1
References
Scientific
Name
Helianthus
annuus
Nicotiana
tabacum
Lycopersicon
esculentum
Ullucus
tuberosus
Triticum
aestivum
Countries
Family
Asteraceae
Solanaceae
(Angulo and Weigert 1975b)
Solanaceae
Chile,
Peru
Chile
Basellaceae
Ecuador
Poaceae
Chile
(Larrain-S. 1996; Angulo and Weigert 1975b; Castillo and Angulo 1991)
(Lamborot et al. 1995; Larrain-S. 1996)
(Vimos-N. et al. 1998)
(Larrain-S. 1996)
35
Host plants listed in Riley 1998 or the APHIS PIN-309 database of interceptions at United States border ports have not been reported in the published literature
unless otherwise noted.
The common names of plants reported in the PIN-309 database were found in the Plants Database at http://plants.usda.gov published by the Natural Resources
Conservation Service.
Figure 8: Acreage of crops that could support populations of Copitarsia species.
36
Figure 9: Acreage of crops (excluding corn, flax, sunflower, and wheat) that could support populations of Copitarsia species.
37
USDA-APHIS
Closely Related Native Species
Copitarsia probably feeds on a wide variety of native non-crop plants in Central and South
America, however we found no specific references to these plants except for Polygonum
segetum (Castillo and Angulo 1991; Zenner de Polenia 1990). Instead, Copitarsia is
reported to feed on “weeds”. We conducted a search of the PLANTS database (USDA
1999) and searched for plants native to the United States that are in the same genera as
crop plants fed upon by Copitarsia. We found 698 plant species in 19 genera (Table 11).
The PLANTS database also gave the states where the plants were found, and all states in
the continental United States were represented. In summary, native plants closely related
to plants known to be consumed by Copitarsia are present in all of the lower 48 states,
providing possible sites for establishment of Copitarsia.
Table 11: Species of plants native to the United States that are in the same genera as crop
plants fed upon by Copitarsia.
Genus
Allium
Chenopodium
Daucus
Dianthus
Fragaria
Helianthus
Lactuca
Linum
Lolium
Malus
Nicotiana
Physalis
Polygonum
Rubus
Simmondsia
Solanum
Trifolium
Vicia
Vitis
Total
Number of Species
Native to the U.S.
87
30
1
1
5
62
9
30
1
9
6
25
51
211
1
56
73
12
28
698
Common Name
onion
goosefoot/ lambs quarter
carrot
Pink/carnation
strawberry
sunflower
lettuce
flax
ryegrass
apple
tobacco
ground cherry
knotweed
blackberry
goatnut
Nightshade/potato
clover
vetch
grape
Crop Phenology and Susceptible Stages
Because insects in the genus Copitarsia have the potential to feed on such a wide variety
of crop and native plant species, in many parts of the country, plants that could support
populations of Copitarsia would be present throughout the year. In the more northerly
parts of the country, no host plants would be available during the winter (in addition to
freezing temperatures) making the establishment of Copitarsia highly unlikely.
38
Risk Assessment of Copitarsia spp.
Is Commodity Processed in Some Way that Would Reduce Pest Risk?
The fate of commodities once they reach the United States can have a profound impact on
the probability of pest establishment, especially for a solitary, sexually reproducing insect
that enters the United States in the immature stage. A Copitarsia larva must complete
larval development, locate a suitable pupation site, emerge as an adult and find a mate,
locate a suitable host plant, and that host plant must be viable for the duration of the larval
stage of the progeny.
We could not find any published information about the fate of produce potentially
harboring Copitarsia larvae in the United States. Some produce goes immediately to
processing plants where it is quick-frozen or pickled (Joe Davidson, personal
communication). Freezing or pickling is lethal to Copitarsia larvae. However, before
produce is processed, infested material is often removed and placed in large piles of culls.
These cull piles may serve to concentrate larvae and increase the risk of establishment.
Produce for fresh consumption would probably go to warehouses, to supermarkets (subject
to culling at both locations), and then to homes where it would be consumed or discarded.
In addition, many of the commodities on which Copitarsia arrives in the United States are
cut flowers, which we suspect are kept chilled and go to warehouses, then retail stores,
then homes.
The above scenarios are only conjectures by the authors. We do not have good
information on the fate of commodities once they arrive in the United States, but the
probability of an organism establishing a viable population very much depends on its
ability to survive the conditions of shipment and processing and to find a mate and suitable
habitat. For that matter, although Copitarsia larvae arrive alive at the border, we do not
know how viable they are after enduring the rigors of shipment. We propose that studies
need to be done to record the conditions of temperature and humidity during shipment, and
to determine the effects of these conditions on the survival and reproduction of Copitarsia.
In addition, it would be interesting to conduct a study on the fate of produce potentially
infested with Copitarsia once it enters the United States.
39
USDA-APHIS
ABILITY TO SPREAD WITHIN THE UNITED STATES AFTER
ESTABLISHMENT
Natural Spread
As mentioned above, we know little about the ability or propensity of Copitarsia to
disperse, apart from one reference citing ballooning by first instar larvae by C. consueta
(Artigas and Angulo 1973b). However, the ability of Copitarsia to disperse or migrate has
implications pertaining to its ability to become a serious pest in the U.S. Some species of
noctuids undergo long-distance migration within the U.S. Spodoptera frugiperda cannot
survive the cold winters of the northern U.S. because it does not enter diapause. However,
it is still a serious pest in the northern states because it is multivoltine, highly fecund, and it
has the ability to migrate over large distances from southern Texas and Florida (Johnson et
al. 1987). Whether Copitarsia has dispersal tendencies like S. frugiperda or tends to be
more like Helicoverpa armigera, which does not exhibit mass ascents to higher altitudes or
long-distance migration (Riley et al. 1992) is unknown. We would recommend further
study of this important issue.
Movement with Commodities
Self-directed dispersal is not the only means by which Copitarsia could be moved
throughout the United States. Produce that could support populations of Copitarsia are
moved throughout the United States and could serve as a pathway that could speed up
spread of these species once established. We know that Copitarsia move into the U.S. on
infested commodities. This mode of transportation could be important to the dispersal of
the pest within the U.S. as well.
40
Risk Assessment of Copitarsia spp.
POTENTIAL FOR DAMAGE IF ESTABLISHED (POTENTIAL ECONOMIC
IMPORTANCE)
Biotic Potential
The invasion of an exotic organism does not necessarily mean that it will ever cause
economic damage. Populations can establish, but if biotic and abiotic factors do not
promote high population growth, it is unlikely that economic damage will occur. This
section describes the factors affecting the population growth of Copitarsia in the U.S.
Number of Generations Per Year
The few references we could find that describe the number of generations per year suggest
that Copitarsia is multivoltine throughout much of its range. Copitarsia turbata is
described as having 1-3 generations per year on quinoa in Bolivia (Liberman-Cruz 1986).
Copitarsia consueta has three generations per year on lettuce in Argentina (Arce de
Hamity and Neder de Roman 1992) and four generations on rapeseed in Chile (Artigas and
Angulo 1973a). Copitarsia humilis, on the other hand, seems to have only one generation
per year in alfalfa in Chile (Hichins-O. and Mendoza-M. 1976). The multivoltine trait of
the more common Copitarsia species not only increases the likelihood of these species
experiencing high population growth on a local level, but could also increase the rate of
spread throughout the U.S.
Fecundity
Copitarsia consueta females are reported to lay 1,638 eggs per female on artificial diet
(Rojas and Cibrian-Tovar 1994). Copitarsia turbata also has a high fecundity, with
females laying 1038 eggs on artificial diet (Larrain-S. 1996), 572 eggs per female on
lettuce (Arce de Hamity and Neder de Roman 1992), and 1,579 eggs per female on onion
(Velasquez-Z. 1988). Although only 572 eggs were laid on lettuce, the females contained
an average of 1552 ovarioles, suggesting that lettuce is not an ideal host plant.
Generation Time
The number of days required to complete development depends on many factors including
temperature, humidity, and host plant. Copitarsia turbata completed development in 60.7
days on onion at 20.4ºC (Velasquez-Z. 1988), yet it took 82.5 days on artificial diet at the
same temperature. Copitarsia turbata completed development in 42.9 days on lettuce at
24.5ºC (Arce de Hamity and Neder de Roman 1992). In the field C. consueta completed
development in 62 days on potato (Lopez-Avilla 1996a).
Mortality
Mortality affecting populations of Copitarsia in the United States can have a large effect
on the growth of populations and thus on damage, but we can predict this factor the least
precisely of all. A large number of natural enemies attack Copitarsia in its native range
(Appendix VII), and these antagonists are often considered to keep the populations in
check. Some of these natural enemies are present in the United States (Lamborot et al.
1995; Machuca et al. 1989b), but it is probable that the United States does not have the
suite of natural enemies responsible for population suppression in the native range of
Copitarsia. Conversely, there are generalist natural enemies present in the United States
that could easily cause mortality to Copitarsia populations.
41
USDA-APHIS
Reproduction Method (asexual, sexual)
The risk of establishment of an asexually reproducing species is higher than that of a
sexually reproducing species because, in theory, a single asexually reproducing individual
can establish a viable population. Copitarsia reproduce sexually. As a result, a Copitarsia
larva that enters the United States must locate a suitable mate for reproduction. This
reduces the probability of establishment.
Intrinsic Rate of Increase
The intrinsic rate of increase of an insect is defined as the maximal population growth
possible for the species under given abiotic and biotic conditions (Southwood 1978). The
intrinsic rate of increase is a function of the net reproductive rate (the positive or negative
change in population numbers from one generation to the next) and the generation time
(the mean age of females at the birth of female offspring). The intrinsic rate of increase
will be higher when the number of eggs laid is high, mortality is low, and the generation
time is short. All of these factors are typically dependent on abiotic conditions
(temperature, humidity, etc.) as well as biotic conditions such as the host plant on which
the insect is feeding and the presence or absence of natural enemies. Copitarsia is no
exception. It was not possible, given the paucity of information about the biology of
Copitarsia in the literature, combined with the large number of host plants, the large range
of potential climatic conditions, and the confused taxonomy of Copitarsia, to precisely
predict the intrinsic rate of increase for Copitarsia in the U.S.
Although we cannot precisely predict the intrinsic rate of increase of Copitarsia
populations in the United States, knowing their fecundity lets us calculate the level of
mortality necessary to prevent population growth. Populations of Copitarsia will remain
stable or decline if the net reproductive rate is less than or equal to 1. In other words, each
female could have one daughter that survives to reproduce. If each female lays 1500 eggs,
750 of them will be female (assuming that Copitarsia has a 50:50 sex ratio). Only one of
those females can survive to reproduce, meaning that 749 females will have to die before
reaching the age of reproduction for the net reproductive rate to equal 1. That translates to
a mortality rate of 99.87% for each generation. If survival is greater than 0.13% per
generation, the population of Copitarsia will increase.
Type and Extent of Damage
Copitarsia larvae cause both direct and indirect damage that affects both the quality and
yield of various crops (Table 12). In some crops, such as rapeseed, up to 60% defoliation
does not affect yield. In other crops, such as artichoke and cabbage, however, a single
larva can either destroy the plant or make it unmarketable. Other crops, including
asparagus, jojoba, lettuce, potato, quinoa, and tomato also suffer direct damage to the part
of the plant sold as a commodity.
Except for one reference about damage to quinoa (80-90%) and another to artichoke (24%)
we did not discover references to the extent of crop damage caused by Copitarsia (Table
12). Given the variability in pest density from year to year due to changes in biotic and
abiotic factors, one would expect high variability in the damage that could be caused by
Copitarsia. However, given the high reproductive capacity of Copitarsia, the fact that it is
42
Risk Assessment of Copitarsia spp.
polyphagous, it has several generations per year, and it can directly damage marketable
commodities, the potential for inflicting damage is high.
The consequences of invasions by a pest may be indicative of consequences in other
countries. Some pests, such as Bemisia spp. and giant salvinia, have repeatedly become
established outside of their native ranges, with potentially disastrous results. For these
pests, environmental conditions are frequently satisfactory for establishment and the results
of these establishments provide information on the potential economic consequences of
introduction of these pests. Outside of South America, there are no references of
Copitarsia establishing viable populations. There is a brief reference to two fully grown
larvae (both males) found in carnations in London (Lowe 1981), but there is no reference
of populations becoming established in Great Britain. If one assumes that commodities
containing Copitarsia are sent to other countries and that it has not become established to
our knowledge, one has to wonder about the ability of Copitarsia to become established
outside of its native range.
Arce de Hamity and Neder de Roman (1993) report that Copitarsia turbata was found for
the first time in Jujuy, Argentina in 1983 and that it caused serious damage in horticultural
crops in that location. Whether the outbreak was the result of release from natural enemies
as Copitarsia expanded its range, a disruption of natural enemies by insecticides,
introduction to new host plants, or a more favorable climatic conditions that led to the
outbreak is unknown. It is indicative, however, that Copitarsia has the ability to cause
considerable damage when introduced to a new area.
43
Table 12: Damage caused by Copitarsia to various crop plants.
Crop
Alfalfa
Artichoke
Artichoke
Artichoke
Species
Copitarsia
Damage Type
Comments
Direct
Young larvae eat the parenchyma of the leaves, while older larvae eat the
entire buds and leaves.
C. consueta Direct
Up to 24% of marketable heads with damage by Copitarsia.
C. turbata
Direct
Attacks inflorescence, reduces commercialization.
C. consueta Direct
Up to 54% of the artichoke heads damaged by noctuid larvae during the
harvest season. Newly formed flower buds also partially or totally
destroyed by C. consueta.
Asparagus C. turbata
Cabbage Copitarsia
Direct
Direct
Feeds on turiones (?).
damage is quite severe - larvae penetrate the head and feed on interior
leaves - can lead to decay that kills the plant - feeding on unformed heads
leads to lateral buds that do not produce commercial cabbages.
44
Cabbage
Jojoba
Lettuce
Copitarsia Direct
C. consueta Direct
C. turbata
Direct
Melloco
Onion
C. turbata
C. turbata
Indirect
Indirect
Onion
Copitarsia
Indirect
Potato
C. consueta Direct
Potato
C. consueta Indirect
Populations highest at the end of the season, which is detrimental because
they infest the tubers.
Severe attacks occur in the dry periods when the potato plants are young.
Potato
Copitarsia
Damage potato shoots, cutting tender buds to the height of the soil line.
Indirect
Larvae can destroy the plant.
Larvae feed on developing fruit and can destroy capsules and seeds.
Attack begins on external leaves, will go between leaves, one larva will
consume 5.32% of a lettuce plant.
Larvae break the small plants or cuts the leaves
Feeds on inside of onion stalks leaving only the epidermis - hidden within
the leaf - also cuts leaves, drills stems, and perforates tubercles.
Holes for entrance into the leaf provide an entrance for plant pathogens,
which can cause the plant to drop the leaf, even after Copitarsia has left.
Usually only one larva/plant in one leaf - so not too important. Control
justified only when >10% of the plants are infested.
Reference
(Cortes-P. 1972)
(Larrain-S. 1984)
(Larrain-S. 1996)
(Machuca et al. 1990)
(Larrain-S. 1996)
(Monge-V. et al. 1984)
(Monge-V. et al. 1984)
(Quiroga et al. 1989)
(Arce de Hamity and Neder de
Roman 1992)
(Vimos-N. et al. 1998)
(Velasquez-Z. 1988)
(Sanchez-V. and MaitaFranco 1987)
(Sanchez-V. 1985)
(Lopez-A. 1996a)
(Arestegui-P. 1976)
Crop
Quinoa
Species
C. turbata
Damage Type
Comments
Direct
Damage the floral buds and flowers, in addition to buds, stems and leaves.
Reference
(Lamborot et al. 1999)
Quinoa
C. turbata
Indirect
(Liberman-Cruz 1986)
Rapeseed C. consueta Indirect
C. turbata
Tomato
Direct
80-90% damage from lepidoptera if no pesticides applied (where they are
on the pesticide treadmill.
Defoliation up to 60% does not affect yield.
Damages mostly young fruit.
(Artigas and Angulo 1973a)
(Larrain-S. 1996)
45
USDA-APHIS
Effect on Export Markets
Chile began to export fresh artichokes to the United States and Canada in 1983. In 1984,
the exported artichokes reached a maximum of over 400 tons per year. This volume has
since decreased to less than 20 tons per year. One of the main causes cited for the decline
in exported artichokes was the frequent rejection of shipments by the receiving countries
because of quarantine pests, especially noctuid larvae (Machuca-L et al. 1989). Between
84.6% and 100% of the boxes were rejected prior to shipping (Machuca et al. 1990).
Copitarsia consueta was one of the three most abundant noctuids, constituting 85.1% of
the larvae collected from small artichoke heads.
Copitarsia has the potential to affect U.S. export markets as well. We assume that
countries without Copitarsia would reject commodities from the U.S. containing
Copitarsia larvae. The U.S. exports at least 15 commodities on which Copitarsia could
feed (USDA-Economic Research Service 1999). The quantity and value of these
commodities are reported in Table 13. Because Copitarsia is rarely found on some
imported commodities that are host plants (Table 13), it is unlikely that larvae would be
exported from the U.S. on these commodities if the pest were to become established. The
reason Copitarsia is not found on some commodities on which it feeds is that Copitarsia is
unlikely to be transported with the roots of carrots, garlic, onion, or potatoes or with the
seeds of rapeseed. We analyzed only the commodities for which Copitarsia was likely to
be present in export shipments, and determined that 754,939 metric tons of produce worth
$456,787,945 was exported from the U.S. in 1999. The percentage of this market
potentially affected by Copitarsia depends on the severity of pest outbreaks in the U.S. and
is difficult to determine.
Table 13: Quantities and values of commodities on which Copitarsia could be found
exported from the U.S. in 1999.
Commodity
Asparagus
Berries
Broccoli
Cabbage
Carrots
Cauliflower
Corn
Garlic
Grapes
Lettuce
Onion
Peppers
Potatoes
Rapeseed
Tomatoes
Quantity Exported in
1999 (Metric Tons)
17,268
79,822
149,417
41,281
119,015
86,874
51,814,105
8,911
238,609
313,969
260,499
66,308
271,509
154,008
151,657
Value in 1999
$49,947,109.00
$14,953,491.00
$98,633,755.00
$16,143,400.00
$67,031,614.00
$58,574,810.00
$4,933,087,772.00
$11,439,083.00
$308,595,978.00
$159,422,609.00
$81,268,654.00
$59,112,771.00
$85,178,691.00
$36,607,919.00
$122,675,361.00
46
Copitarsia frequently found on
Imports?
yes
yes
yes
yes
no
yes
no
no
no
yes
no
yes
no
no
no
Risk Assessment of Copitarsia spp.
Effect on Control Costs and on Ongoing IPM Programs
To adequately address this in a quantitative fashion, we would have to conduct an
extensive economic analysis, including information on which crops would be affected,
how many acres, and the percentage damage/yield loss, etc. That sort of analysis is beyond
the scope of this short-term project and would be difficult to conduct given the large
uncertainties involved. However, pesticides are the most likely control tool, as they are
frequently recommended in areas where Copitarsia is currently established (Artigas and
Angulo 1973a; Machuca et al. 1990; Perlta-S. 1987; Cortes-P. et al. 1972; Sanchez-V. and
Maita-Franco 1987; Liberman-Cruz 1986; Quiroz-E. 1977; Vimos-N. et al. 1998; Munro
1968; Munro 1954; Zenner de Polenia 1990). Treatment with insecticides increases the
cost of raising a crop, as well as disrupting natural enemies of all pests on that crop,
potentially disrupting ongoing Integrated Pest Management (IPM) programs. IPM
programs are in place in CA for five crops potentially attacked by Copitarsia (alfalfa, cole
crops, lettuce, potatoes, and tomatoes). These ongoing programs could be disrupted by
pesticides applied to control Copitarsia.
Environmental Damage (Direct and Indirect)
Once established, Copitarsia would have the potential to damage the environment both
directly, through feeding on native plants, and indirectly because of environmentally harsh
methods used to control pest populations. It has already been noted (Table 11) that there
are nearly 700 plants that are native to the U.S. that are in genera known to be attacked by
Copitarsia larvae. Seven of the native species at risk from Copitarsia are listed as
threatened or endangered by the U.S. Fish and Wildlife Service (Table 14). Indirect
environmental damage could be caused when pesticides applied to control Copitarsia leave
the treated field, either as drift or contaminated groundwater, and affect other species in the
ecosystem.
Table 14: Plants native to the U.S. that are in genera consumed by Copitarsia and are
listed by the U.S. Fish and Wildlife Service as Threatened or Endangered.
Plant Species
Historic Distribution in U.S.
Status
Allium munzii (Munz’s onion)
CA
Endangered
Helianthus eggertii (Eggert’s sunflower) AL, KY, TN
Threatened
Helianthus paradoxus (Pecos sunflower) NM, TX
Threatened
Helianthus schweinitzii (Schweintz’s
NC, SC
Endangered
sunflower)
Trifolium amoenum (showy Indian
CA
Endangered
clover)
Trifolium stoloniferum (running buffalo
AR, IL, IN, KS, KY, MO, OH, WV Endangered
clover)
Trifolium trichocalyx (Monterey clover)
CA
Endangered
Capacity to act as a Vector for Other Pests or Diseases
There is evidence that larvae of Mamestra brassicae can move bacteria from one plant to
another (Lilley et al. 1997). There is no reference of Copitarsia adults or any other
noctuids serving as vectors of other pests or plant diseases, and it is unlikely that
Copitarsia would do so over any great distances if introduced into the U.S..
47
USDA-APHIS
RISK ASSESSMENT
The bulk of this document deals with the likelihood of Copitarsia successfully invading
the U.S. and causing economic and/or environmental damage. APHIS has developed a set
of guidelines that address the same fundamental questions (Appendix III in Biological
Assessment and Taxonomic Support 1997). The guidelines are intended to formalize the
process of qualitatively assessing pest risk. We have applied the guidelines to Copitarsia
in an attempt to provide summaries of the various components that contribute to pest risk.
We recognize that APHIS guidelines have been criticized for relying on “highly subjective
and uncharacterized expert judgment in the assignment of risk” (National Plant Board
1999). Traditional APHIS risk assessments describe risk by consensus of a scientific
panel. Generally, differences of opinion among panel members or the degree of
confidence in the assessment are not described.
To more formally characterize variability and uncertainty in risk ratings, we asked several
colleagues to review the data we had gathered on Copitarsia and to complete a pest risk
survey. For each risk element within the survey, we asked that the reviewer provide a
ranking of high, medium, or low and an indication of their level of confidence in this
assessment (again high, medium, or low). We provided criteria for each risk element,
following APHIS risk assessment protocols (USDA-APHIS 1997). Because the risk
assessment deals with the genus as a whole, we asked reviewers to assume that if one
species of Copitarsia satisfies the criterion, the entire genus satisfies the criterion (i.e., a
worst case scenario). When assigning the level of confidence, we asked reviewers to use
the following criteria: High = data are adequate AND analyses are appropriate to draw a
conclusion; Medium = data are available, but insufficient OR analyses are questionable; or
Low = no data are available OR analyses are incorrect. Assigning a high risk rating with a
low degree of confidence was acceptable, or conversely, a low risk rating with a high
degree of confidence.
Below we present the findings of our survey in a format that is consistent with previous
APHIS risk assessments. At the time this report was prepared, eight individuals had
responded to the survey. For each element, our concluding summary of risk and the level
of confidence in the assessment is based on the maximum number of respondents (i.e., the
mode). Frequency distributions for all responses are presented in Figure 10.
Following APHIS guidelines, the assessment is divided into the consequences of
introduction (i.e., the consequences of pest invasion) and the likelihood of introduction (i.e.
invasion). For the most part, the consequences of introduction are dependent on biological
characteristics of the pest. The likelihood of introduction, on the other hand, may vary
considerably from one port to another, and a risk based resource allocation model must be
able to access data that would allow different assessments of risk to be calculated for each
port of entry. Where appropriate, we indicate how risk might be more accurately assessed
or where supplemental research might be valuable. We do not attempt to develop new
criteria for estimating pest risk; such an effort is beyond the scope of this short-term
project.
48
Risk Assessment of Copitarsia spp.
Figure 10: Assessment of risk and confidence in the assessments by a panel of eight
experts.
10
8
6
Low
Medium
High
Risk
Frequency of Rating
4
2
6
0
Confidence
5
4
3
2
1
0
Climate/Host Host Range
Dispersal
Economic Environmental
10
Risk
8
6
Frequency of Rating
4
2
0
10
Confidence
8
6
4
2
0
Quantity of
Survive
Survive
Commodity Post-Harvest Shipment
Imported Treatment
Detect at
Port of
Entry
Risk Element
49
Moved to
Suitable
Habitat
Find Host
Plant
USDA-APHIS
Consequences of Introduction
Table 15. Evaluation of the consequences of Copitarsia introduction based on several risk
elements assessed in an expert survey and in previous APHIS risk assessments.
Current Study
Previous Assessments
Element
Risk
Confidence
(% of respondents)
(% of respondents)
Risk1
Risk2
Risk3
Climate/Host
High (62%)
Medium (62%)
High
High
Medium
Host Range
High (100%)
High (62%)
High
High
High
Dispersal
High (62%)
Medium (62%)
High
High
Medium
Potential
Economic
High (88%)
High (62%)
High
High
Medium
Impact
Environmental
High (100%)
Med-Low (38% each)
Medium Medium
High
Impact
Overall
High
Medium
High
High
High
1 (Cave and Redmond 1997b)(Brassica from Mexico)
2 (Cave and Redmond 1997a)(Brassica from Central America)
3 (USDA-APHIS-PPQ-BATS 1997)(Pisum from Mexico)
Climate/Host
This risk element attempts to describe the area that might be impacted by the pest. The
majority of the respondents felt it was likely for this species to survive in four or more
plant hardiness zones (Table 15; Fig 10). Consequently, the element is classified as high
risk. Two of three previous risk assessments also gave this element a high rating.
However, the majority of respondents also felt that data were insufficient or analyses were
questionable. Consequently, the overall confidence in the assessment was medium.
CLIMEX analysis predicted a greater than 50% probability that Copitarsia could survive
in most of California, which contains at least four plant hardiness zones. We believe that
further research is necessary on the response of Copitarsia to temperature and soil
moisture before a complete prediction of its range in the U.S. can be made. Such results
would increase our confidence in the ranking for this element. Directly predicting the
portion of the U.S. likely to be infested by a pest would, in our opinion, be a better
indicator of risk than the current system of relying on the number of potential plant
hardiness zones.
Host Range
This risk element accounts for the diversity of domestic plants that could be affected by the
pest. All respondents felt that Copitarsia could attack multiple species within multiple
plant families and assigned a high risk rating in this category, which is completely
consistent with previous risk assessments (Table 15; Fig 10). The majority of respondents
also felt that adequate data were available and analyses were appropriate to draw a
conclusion.
50
Risk Assessment of Copitarsia spp.
Dispersal Potential
This risk element pertains to the rate at which a new pest will achieve its maximum range
in the U.S.. This category not only includes rapid pest movement (> 10 km per year) but
also a high reproductive potential (many generations per year, many offspring per
reproduction, and a high innate capacity for population increase). If a pest exhibits both
characteristics it receives a high rating; one characteristic leads to a medium ranking. The
majority of respondents gave this risk element a high rating but felt that data were
insufficient or analyses were questionable to reach a firm conclusion (Table 15; Fig 10).
Two of three previous risk assessments also classified this element as high risk. Little
evidence exists on the ability of Copitarsia to disperse on its own.
Economic Impact
There are three economic impacts considered by this risk category; lower yield of host
crop, lower value of the commodity, and loss of markets. A high rating is given when the
pest could cause all three impacts. In its native range, Copitarsia has been shown to lower
yields, lower the value of the commodity, and affect export markets. The majority of
respondents felt that all three impacts were likely in the US (as did two of three previous
risk assessments). The overall confidence in this assessment was also considered high
(Table 15; Fig 10).
Environmental Impact
Five potential impacts are listed by APHIS for consideration: direct environmental
impacts, direct impacts on endangered or threatened species, indirect impacts on
endangered or threatened species, initiation of disruptive control programs (pesticides), and
initiation of release of non-indigenous biological control programs. A high rating is
provided if two or more of the criteria apply. In contrast to two of three previous risk
assessments, all respondents assigned a high rating (Table 15; Fig 10). However, an equal
number of respondents considered their level of confidence in the risk rating to be medium
or low.
51
USDA-APHIS
Likelihood of Introduction
Table 16. Evaluation of the likelihood of Copitarsia introduction based on several risk
elements assessed in an expert survey and in previous APHIS risk assessments.
Current Study
Previous Assessments
Risk
Confidence
(% of respondents) (% of respondents)
Risk1
Risk2
Risk3
Quantity of
Medium Medium
Low
High (100%)
High (100%)
commodity
imported
annually
Likelihood
High
High
Medium
High (62%)
Low (50%)
survive postharvest
treatment
Likelihood
High
High
High
Medium (50%)
Low (88%)
survive
shipment
Likelihood not
High
Medium
Low
High (75%)
High (75%)
detect at port
of entry
Likelihood
High
High
Medium
High (62%)
High/Low (38% each)
moved to
suitable habitat
Likelihood find
High
High
Medium
High (75%)
Medium (50%)
suitable host
Overall
High
High
Medium
High
Low
1 (Cave and Redmond 1997b)(Brassica from Mexico)
2 (Cave and Redmond 1997a)(Brassica from Central America)
3 (USDA-APHIS-PPQ-BATS 1997)(Pisum from Mexico)
Quantity of Commodity Imported Annually
The APHIS guidelines about the quantity of a commodity imported refer to one specific
commodity from one country imported into the entire U.S.. Our pest based risk assessment
concerns a genus that is found on many commodities from many countries of origin. The
APHIS criterion is that for a pest to be rated High, over 100 forty-foot containers of the
commodity enter the U.S. per year. All respondents felt that the criterion was satisfied for
the total of all commodities on which Copitarsia could enter (Table 16; Fig 10). It would
even be true for some commodities from some countries entering a single port. All
respondents were highly confident in the assessment.
Certain commodities from specific countries may not meet this standard, which may
explain why previous APHIS risk assessments assigned risk ratings of low or medium to
this element. If one were to assess the risk of entry of Copitarsia for a model of risk based
resource allocation, one must separately assess the risk of entry based on particular
commodities coming from particular countries. Populations of Copitarsia may be different
52
Risk Assessment of Copitarsia spp.
on the same commodity coming from various countries due to differences in farming
practices, climate, and natural enemies. Also, various commodities coming from the same
country will differ in their suitability as hosts for Copitarsia and in their pre-shipment
treatment that will affect Copitarsia numbers.
Likelihood Survive Post-harvest Treatment
For this element, a risk rating of low is assigned if the likelihood of Copitarsia surviving
post-harvest treatments is less than 0.1%, medium if 0.1% to 10%, and high if greater than
10%. The majority of the panel felt the ranked this element as high (Table 16; Fig 10).
However, the panel also felt that no data were available or analyses were incorrect so that
the level of confidence in this assessment was low. Two of three previous risk assessments
also ranked this element as high.
Likelihood Survive Shipment
For this element, a risk rating of low is assigned if the likelihood that Copitarsia that
survive post-harvest treatments will also survive shipment is less than 0.1%, medium if
0.1% to 10%, and high if greater than 10%. The majority of the panel felt the ranked this
element as medium but again felt that little data were available to draw a firm conclusion
(Table 16; Fig 10). The overall confidence in the assessment of this risk element is low.
We know that Copitarsia larvae survive shipment, because only live larvae are reported in
the PIN-309 database. We do not know the percentage of all Copitarsia larvae that survive
shipment or whether these live larvae are healthy. If cut flowers are chilled during
shipment, this could affect the viability of Copitarsia larvae. We feel that a detailed study
of shipping conditions and their effects on the health of Copitarsia larvae would be an
important area for future study.
Likelihood not Detect at Port of Entry
PPQ officers at ports of entry are instructed to inspect 2% of a given shipment arriving at a
U.S. port. In other words, they should inspect 2 boxes out of every 100 boxes of a certain
commodity. To have a 95% probability of detecting a pest species given this rate of
inspection, often 25 % or more of the boxes must be infested. Smaller shipments may be
even more heavily infested but still pass inspection. The majority of respondents felt that
more than 10% of the viable larvae that arrived at a port would not be detected by routine
inspections and thus assigned a high risk rating (Table 16; Fig 10). The majority of
respondents also felt that adequate data were available and appropriately analyzed to reach
a conclusion. Previous risk assessments were divided on this issue with rankings for this
element ranging from low to high.
Likelihood Moved to Suitable Habitat
After Copitarsia larvae enter the U.S., they must undergo further rigors of shipment, and
not all final destinations will have climates suitable for survival. Nevertheless, the
majority of the panel felt that more than 10% of the viable larvae that were not detected at
a port of entry would be transported to a climatically suitable habitat (Table 16; Fig 10).
Again, a high risk rating was assigned. Equal numbers of individuals on the panel had
high or low confidence in their decisions.
53
USDA-APHIS
Likelihood find Suitable Host
The majority of the panel felt that the probability of larvae finding a suitable host plant
once they arrived in a climatically suitable habitat was >10%; consequently a rating of
high was assigned (Table 16; Fig 10). Two of three previous risk assessments also reached
the same conclusion. However, the majority of respondents to our survey only felt
moderately confident in their assessment of risk. Copitarsia spp. are reported feeding on
over 39 crop plants and there are approximately 700 plants native to the U.S. that are in
genera fed upon by Copitarsia. While higher acreages of the crops are found along the
Pacific coast and in parts of the east coast, plants with the potential to be fed on by
Copitarsia can be found in just about every county in the U.S.
Overall Assessment of Risk
After combining consequences of introduction with the likelihood of introduction, the
Overall Risk Potential for Copitarsia is high using the point system guidelines provided by
APHIS (not presented for simplicity), but confidence in this assessment is moderate to low.
This project formally measures the degree of variability and confidence in the assignment
of risk ratings for Copitarsia. We believe this effort will contribute directly to the
preparation of future risk assessments and the evolution of general risk assessment
procedures within APHIS. Several previous APHIS commodity risk assessments have
assessed the risk posed by Copitarsia (Cave and Redmond 1997a; Cave and Redmond
1997b; USDA-APHIS-PPQ-BATS 1997). Other APHIS risk assessments associated
Copitarsia with a commodity for potential import but did not proceed to formally evaluate
the risks this insect might pose (Chawkat 1997 and Stewart et al. 1996). In some cases,
poor taxonomy of the pest may have contributed to a reluctance to assign risk ratings.
Until additional information becomes available, we believe this document justifies a high
risk rating for the genus as a whole. Resolving the taxonomy of the genus is critical to
evaluate individual species. Risk ratings assigned with medium to low confidence
highlight vital topics for future study. Furthermore, as noted above, separate risk
assessments do not always assign the same degree of risk to Copitarsia. With rare
exceptions (e.g., the volume of commodity that is to be imported), rationale for the
difference in risk ratings is not provided. We hope the current assessment provides a
resource for future risk assessors who confront Copitarsia.
54
Risk Assessment of Copitarsia spp.
AREAS RECOMMENDED FOR FUTURE STUDY
Needed to refine Copitarsia risk assessment and management:





Define taxonomy of the genus Copitarsia: Without this information we cannot
accurately define the distribution of Copitarsia species or know when we are
intercepting pest species at our borders. Accurate taxonomy is also fundamental to
apply studies on the biology of Copitarsia to better predict the dynamics of various
Copitarsia species.
Determine the response of Copitarsia to climate conditions: Knowledge of the
effect of temperature and moisture on Copitarsia survival and reproduction will
allow a more accurate estimate of the potential range of Copitarsia in the United
States and the propensity to outbreak in its native range.
Determine the effect of shipment and processing on the probability of
establishment of Copitarsia : Shipping conditions, processing, and final
disposition (pickling, freezing, culling, sale to groceries) are likely to affect the
probability of Copitarsia surviving, reproducing, and establishing. Results of these
studies will contribute to risk assessment and mitigation.
Define the dispersal potential of Copitarsia adults: This study will allow us to
predict the probability of natural spread into and within the U.S. At this point, we
cannot completely discount the possibility that Copitarsia has naturally arrived in
the US.
Determine the distribution of Copitarsia: This study will answer the following
questions: 1) Is it found in countries where we so far have received few shipments?
2) Is its range in Mexico limited and would explain the lack of interceptions in
Nogales and San Diego? 3) Given the high probability that Copitarsia evades
detection at the border and its proximity in Mexico, is it established in the U.S.?
Needed to refine risk-based resource allocation:


Conduct Risk Assessments of organisms with different characteristics:
Characteristics important in assessing the risk of Copitarsia to U.S. agriculture and
natural areas may not be different for organisms with other life strategies
(pathogens, borers, mites, etc.).
Define the minimum number of individuals necessary to establish a viable
population for an invading species.
55
USDA-APHIS
ACKNOWLEDGMENTS
Gathering data, translating documents from Spanish to English, and creating tables and
graphs was a large undertaking. The final product was much enhanced by the assistance of
the following personnel: AZ: Susan Thompson-McHugh, Nicole Harris, Mitch Colletto;
MN: Fernando Muñoz-Quesada, Rebecca Simmons, and Lori Thompson. Special thanks
go to Leroy C. Gould for advice on statistical analysis of categorical data. Nick Colletto
provided valuable assistance proofreading this document. We thank the following
individuals for reviewing this document and participating in the risk assessment survey:
Fernando Muñoz-Quesada, Rebecca Simmons, Russ Stewart, Michelle Walters, and James
Berry.
56
Risk Assessment of Copitarsia spp.
APPENDIX I: PHOTOGRAPHIC AID FOR IDENTIFICATION OF COPITARSIA
LARVAE
Figure 11: Picture used by APHIS-PPQ officers to assist in the identification of Copitarsia
larvae.
57
USDA-APHIS
APPENDIX II: TAXONOMY OF THE GENUS COPITARSIA
(Prepared by Rebecca Simmons, Dept. of Entomology, University of Minnesota)
Species often confused with Copitarsia
Larval identifications can be difficult. Copitarsia is often confused with some members of
Spodoptera and Peridroma. Spodoptera, unlike Copitarsia, has dark bars only at the base
of the upper medial setae on the mesothorax and metathorax. Spodoptera also has dark
setae, while Copitarsia has white setae. Spodoptera also has a spot at the lower medial
setal position on the mesothorax, and four setae that form a straight line on the head.
Peridroma can be distinguished from Copitarsia by having light brown instead of white
setae. Peridroma also has a bar at the base of only the lower medial setae on the
metathorax. The head of Peridroma is distinguishable because it has two brown medial
stripes and the spinneret has four to six spinules at the tip.
Adult Copitarsia have been described as members of Agrotis (Walker 1857). Agrotis can
be distinguished from Copitarsia by its forewing markings, especially those associated
with the reniform spot (Covell 1984). These markings are black bars that are found near or
covering the reniform spot (Covell 1984). In contrast, Copitarsia adults have reniform and
orbicular spots outlined in black. Agrotis larvae of appear to be distinctive from those of
Copitarsia. Agrotis larvae have a black head, and no bars between setal bases.
Taxonomic and Systematic Challenges of Copitarsia
Copitarsia is often described as a difficult systematic problem (Poole 1989). One
immediate concern is that C. turbata (Herrich-Schäffer) is not an available name. Poole
(1989) found that the original combination Polia turbata is traditionally dated from 1845,
but the combination does not occur on the plate in Herrich-Schäffer (1845). The
combination appears in a later index to the 1845 volume (published in 1855). Thus,
Mamestra decolora Guenee, published in 1852, is the oldest name for the type species of
Copitarsia. Poole (1989) chose not to rectify this situation for two reasons: the economic
importance of Copitarsia and the need for a generic revision. Castillo and Angulo (1991)
did not comment on this issue in their revision, leaving C. turbata (Herrich-Schäffer) as
the name for the type species. Designating C. decolora as the new name for the type
species will make literature searches difficult, alter collections, and impact future
agricultural studies of this pest genus.
Additionally, Copitarsia contains many synonymous species, which obscure the actual
number of species included in this group (M. Pogue, SEL-USDA; M. Honey, NHMLondon, pers. comm.). Köhler’s species (C. basilinea, C. clavata) are thought to be
synonyms (Pogue pers. comm.). Castillo and Angulo (1991) also may have created
synonymous species by over-describing morphological variation throughout a geographic
range (i.e. C. anguloi, C. paraturbata). As the same species has been described multiple
times, these synonymous species inflate the number of species in Copitarsia. Synonymous
species also impact risk assessment studies, as there may actually be fewer agricultural
pests in Copitarsia than the current taxonomy suggests.
58
Risk Assessment of Copitarsia spp.
Although larval keys exist to distinguish Copitarsia from other noctuid genera, there
appear to be few distinguishing external characters for identifying adult Copitarsia. Also,
Copitarsia may be an artificial assemblage of species. Without a recent phylogenetic
revision of Copitarsia, shared derived characters (synapomorphies) cannot be established.
These synapomorphies are key to test if Copitarsia is a real evolutionary unit
(monophyletic) and to identify diagnosable characteristics for identifying adult Copitarsia.
Additionally, because Copitarsia is difficult to identify, it is likely that undescribed species
of Copitarsia are misplaced in other noctuid genera. These Copitarsia may even be placed
in unrelated genera in completely different noctuid subfamilies. Again, externally
diagnosable characters will be useful in finding misplaced species of Copitarsia.
The placement of Copitarsia in Cuculliinae is not known. Management plans for
Copitarsia would be enhanced by knowledge of the genera’s closest relatives or sister
groups. If these sister groups are polyphagous pests that can be controlled by certain
strategies (pesticides, certain parasitoids) then controlling Copitarsia can be accomplished
by similar strategies. Also, a recent morphological study (Weller and Simmons in prep.)
indicates that the cuculliines may not be a monophyletic unit. If this is the case, Copitarsia
may be more closely related to genera that it has been previously associated with (i.e.
Agrotis, Euoxa) than other cuculliines. This knowledge may alter current control strategies
for Copitarsia.
Finally, a major issue facing the management of Copitarsia importation into the US is that
larval associations with adults are not known. Because there is a lack of rearing studies
and life history information for Copitarsia, it is impossible to identify pest species versus
benign species. Misidentification of pest species could result in accidental rejection of
agricultural products or, worse, the accidental introduction of pest species into the US.
Role of systematics in Agriculture
Systematic studies have been extremely useful in managing agricultural systems.
Understanding the evolutionary relationships (phylogeny) of a pest species is often critical
for effective control of that pest. For example, tobacco budworms, which are frequently
found on commodities imported from Central and South America, were thought to be a
complex of only three species. A systematic study revealed that this complex actually is
comprised of 12 species, which alter interception and control strategies (Systematics
Agenda-2000 1994). Systematic studies can also provide clues on what control measures
may be effective for pest species. Control of the cottony-cushion scale on citrus in
California was ineffective until a systematist provided information about the phylogeny of
the scale and its relatives. With this information, an Australian lady beetle was found to be
a possible biological control agent of the scale (Dahlsten 1986). The lady beetle was
introduced into California and the damage on citrus by the scale was checked. These
examples indicate that a systematic study of Copitarsia will provide information that is
vital to control its introduction into the US and to manage its impact on economically
important crops.
Recommendations
A modern, systematic treatment of Copitarsia is necessary to formulate future risk
assessments and to control this pest genus. We recommend a revision that incorporates
both anatomical and molecular information. The anatomical study should include data
59
USDA-APHIS
from all life stages of Copitarsia. Including larval data will elucidate larval/adult
associations. Morphological data will not only provide synapomorphies for Copitarsia, but
it will also provide information for diagnoses and keys to identifying species. A molecular
study using mitochondrial DNA will provide complimentary information for the
evolutionary relationships (phylogeny) of Copitarsia. Additionally, a molecular study that
includes exemplars from Cuculliinae, Noctuinae, and Hadeninae will also establish sister
taxa of Copitarsia and test the monophyly of the Cuculliinae. Finally, a phylogeny using
both data will be a powerful tool for examining biological questions, such as the
biogeography and evolution of host use in Copitarsia that will impact agricultural control
strategies.
60
Risk Assessment of Copitarsia spp.
APPENDIX III: REFERENCES CITED TO DETERMINE GEOGRAPHIC
DISTRIBUTION OF COPITARSIA
Country
References
Argentina (Arce de Hamity and Neder de
Roman 1992)
(Arce de Hamity and Neder de
Roman 1993)
(Castillo and Angulo 1991)
Bolivia
Chile
Country
Colombia
(Lopez-A. 1996b)
Costa Rica
(Munro 1968)
Ecuador
(Angulo and Weigert 1975b)
Guatemala
(Angulo and Weigert 1975a)
Latin America
(Zenner de Polenia 1990)
(Angulo et al. 1985)
(Castillo and Angulo 1991)
(Vimos-N. et al. 1998)
(Castillo and Angulo 1991)
(Angulo and Weigert 1975b)
(Castrejon-G. et al. 1998)
Mexico
(Apablaza-H. 1984)
(Avila-R. 1961)
(Carrillo-S. 1971)
(Apablaza and Stevenson 1995)
(Castillo and Angulo 1991)
(Arce de Hamity and Neder de
Roman 1992)
(Artigas and Angulo 1973a)
(Castrejon-G. et al. 1998)
(Guevara-A. and Cervantes 1991)
(Aruta-M et al. 1974)
(MacGregor and Gutierrez 1983)
(Castillo and Angulo 1991)
(Monge-V. et al. 1984)
(Cortes 1976)
(Riley 1998b)
(De la Maza-Z. 1986)
(Rojas and Cibrian-Tovar 1994)
(Duran-M 1972)
(Grez 1992)
(Castillo and Angulo 1991)
(Lopez-A. 1996a)
(Neder de Roman and Arce de
Hamity 1991)
(Liberman-Cruz 1986)
(Anonymous 1921)
References
(Rojas et al. 1993)
Peru
(Alcala-C. 1978b)
(Hichins-O. 1972)
(Angulo and Weigert 1975b)
(Hichins-O. and Rabinovich 1974)
(Anonymous 1944)
(Hichins-O. and Mendoza-M.
1976)
(Lamborot et al. 1995)
(Arce de Hamity and Neder de
Roman 1992)
(Arestegui-P. 1976)
(Lamborot et al. 1999)
(Castillo and Angulo 1991)
(Larrain-S. 1984)
(Gomez-T. 1972)
(Larrain-S. 1996)
(Leyva-O. and Sanchez-V. 1993)
(Larrain-S. and Araya-C. 1994)
(Sanchez-V. and Aldana-M. R.
1987)
(Sanchez-V. and Maita-Franco
1987)
(Valencia and Valdivia 1973)
(Loo and Aguilera 1983)
(Machuca-L. et al. 1988)
(Machuca et al. 1989b)
(Velasquez-Z. 1988)
(Machuca-L et al. 1989)
(Machuca et al. 1990)
(Wille-T. 1943)
Venezuela
(Olivares and Angulo 1995)
61
(Castillo and Angulo 1991)
USDA-APHIS
Country
Chile
References
Country
(Opazo 1914)
(Porter 1980)
(Prado 1991)
(Quiroga et al. 1989)
(Quiroz-E. 1977)
(Vargas-C. 1972)
62
References
Risk Assessment of Copitarsia spp.
APPENDIX IV: METHODS USED FOR CLIMEX ANALYSIS
Climate Matching. We identified 12 target locations in South America that were well
within the known distribution of Copitarsia and were listed in the Climex database (Table
17). Locations ranged in elevation from near sea level (Valdiva, Chile) to 1640m above
sea level (Merida, Venezuela). The majority of locations were in Chile, but the entire
western half of South America was represented in the analysis.
Table 17: South American cities with established populations of Copitarsia spp. used for
climate matching
Country
City
Elevation (m)
Longitude
Latitude
Venezuela
Merida
1640
71.2 W
8.6 N
Peru
Lima
120
77.0 W
12.1 S
Colombia
Bogotá
2645
74.1 W
4.6 N
Chile
Balmaceda
525
71.7 W
45.9 S
Chile
Concepcion
12
71.3 W
36.8 S
Chile
Curico
225
71.2 W
35.0 S
Chile
Puerto Monett
81
73.1 W
41.4 S
Chile
Santiago
520
70.7 W
33.5 S
Chile
Temuco
114
72.7 W
38.8 S
Chile
Valdiva
5
73.3 W
39.8 S
Chile
Valparaiso
41
71.7 W
33.1 S
Argentina
Mendoza
800
68.8 W
32.8 S
We then classified 169 locations in North and South America as suitable for establishment
(coded 1) or not suitable (coded 0). Of the 74 locations inferred to be suitable, 36 occurred
in South America and 38 in North America. A location must have been within or on the
fringe (within ~30km) of the described distribution of the genus to be considered
appropriate for establishment. In North America, all suitable locations occurred south of
the 24th parallel. Conversely, 95 locations in Canada and the northern US with maximum
air temperatures <0°C for 2 months were classified as unsuitable. The current
distribution of the genus suggests that the insect is not able to survive extended periods of
sub-zero temperatures.
Climatic conditions at the 12 target locations were independently compared to 330
locations in North America and 109 locations in South America. Similarity was expressed
in a number of indices described in Sutherst and Maywald (1985). Similarity indices were
calculated for minimum air temperature, maximum air temperature, total precipitation,
rainfall pattern, and relative humidity, respectively. All indices assume a value between 0
and 100%, inclusive. In addition, Climex calculated a Match Index which provided a
measure of overall climatic similarity across environmental parameters. For our analyses,
the influence of relative humidity was excluded from the Match Index. To provide a final
characterization of each of the 401 locations in North and South America, we determined
the maximum degree of similarity to any one of the 12 target locations. The maximum
degree of climatic similarity was evaluated separately for each climatic index.
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USDA-APHIS
For data analysis, we randomly selected 2/3 of the 169 locations classified on the presence
or absence of Copitarsia populations; the remaining 1/3 was used for model validation.
Data were analyzed using logistic regression (PROC LOGISTIC; SAS Institute 1995).
This analytical tool relates the probability of an event (i.e., establishment) to several
independent explanatory variables (Hosmer and Lemeshow 1989). When applied to
Climex output, the tool also provides a quantitative interpretation of each similarity index
(Venette and Hutchison 1999). Logistic regression produces a model with the form:
 p 
  b  m1 X 1  m2 X 2  m3 X 3    mn X n
ln 
1

p


where p is the probability of establishment [0p1], b is the intercept of the line, X1 … Xn
are indices of climatic similarity, and m1 … mn are coefficients describing the slope of the
line. Values for b and m1 … mn are determined through maximum likelihood estimation.
Forward, backward, and stepwise elimination techniques were used to identify similarity
indices for inclusion in the final model. When back-transformed, the model becomes:
1
p
.
 ( b  m1 X 1 m 2 X 2  m 3 X 3  mnXn)
1 e
To validate the model we determined an overall percentage of correct classifications and
the percentage of correctly identified positives, correctly identified negatives, false
positives, and false negatives.
For each location, the probability of establishment and pertinent Climex indices were
geographically referenced and incorporated into a geographic information system
(ArcView 3.1, Environmental Science and Research Institute, Redlands, CA). To generate
isopleths between points, a digital grid with a cell size of 0.25 degrees was placed over a
map of the Western hemisphere. Predicted values for each cell within the grid were
determined by inverse distance weighting of the observed values for the 12 nearest
neighbors.
Population modeling. Climex requires estimates of several parameters to model the
influence of climate on population dynamics (Table 18). One approach is to estimate these
parameters directly from published literature reports (Venette and Hutchison 1999). Such
information for Copitarsia is scarce and incomplete. As a result, we employed an iterative
approach, as per Sutherst and Maywald (1985), to estimate parameters for Climex. To
begin, a test area within the current geographic distribution of the pest was selected. For
our analysis, we focused on Chile because of its climatic diversity. We then selected an
arbitrary set of parameters and used Climex to predict the distribution of “Copitarsia”
within Chile. The predicted distribution was compared to the known distribution of the
pest within the country. Parameters within Climex were repeatedly adjusted until a
qualitatively accurate prediction of the distribution of Copitarsia in Chile was obtained.
64
Risk Assessment of Copitarsia spp.
Table 18: Parameters used by Climex to describe the influence of temperature and
moisture on the population dynamics of Copitarsia
Estimate for
Description
Parameter Copitarsia
Temperature
Minimum required for physiological development DV0
4
Lowest at which population growth is optimal
DV1
9
Highest at which population growth is optimal
DV2
24
Upper limit for population growth to occur
DV3
28
Threshold for the onset of cold stress
TTCS
0
Rate of cold stress accumulation
THCS
0.0005
Threshold for the onset of heat stress
TTHS
30
Rate of heat stress accumulation
THHS
0.001
Moisture
Minimum required for physiological development SM0
0.25
Lowest at which growth is optimal
SM1
0.5
Highest at which growth remains optimal
SM2
1.5
Upper limit for growth to occur
SM3
2.0
Threshold for the onset of drought stress
SMDS
0.05
Rate of drought stress accumulation
HDS
0.02
Threshold for the onset of wet stress
SMWS
1.6
Rate of wet stress accumulation
HWS
0.0015
Because the distribution of Copitarsia within Chile is likely to be influenced by cultural
practices, notably irrigation, two criteria for choosing an acceptable parameter set were
adopted. First, in the absence of supplemental irrigation, the parameters suggest that
Copitarsia should occur only in southern regions of Chile. Second, with irrigation during
the summer months (76 mm/2 wks), the same parameter set should also predict that
Copitarsia could occur in more northern regions of the country (i.e., near the cities of
Antofagasta and Arica). A parameter set that satisfied these criteria was used to model the
performance of Copitarsia in the remainder of North and South America.
In modeling the performance of a species, Climex generates a new set of indices, again
scaled from 0 to 100 (Sutherst and Maywald 1985). In general, these indices describe the
potential for population growth under prevailing temperature and moisture conditions and
the degree of stress a population is likely to experience when conditions are too extreme
(i.e., hot, cold, wet or dry) for population growth. In addition, Climex determines the
number of degree days accrued by a species annually following the method of (Baskerville
and Emin P. 1969). The biological models within Climex were run under two scenarios:
no supplemental irrigation or 76 mm of water every 2 weeks. Because the Climex model
does not allow the option of allowing irrigation only where needed, we recognized that
results from either scenario alone would not be completely accurate. Specifically,
supplemental irrigation in rain-soaked tropical countries would artificially increase the
degree of wet stress experienced by the insect and would not represent local agronomic
practices. To compensate, we combined results from both scenarios by selecting
conditions that would be most favorable for the insect (e.g., conducive to greater growth or
less stress).
65
USDA-APHIS
This set of Climex indices was also analyzed using logistic regression. For this analysis,
13 locations in Chile that had been classified as positive for the presence of Copitarsia
were removed. Due to the nature of the biological models, many of the Climex indices
were interrelated and violated assumptions of independence (i.e., multicolinearity). To
compensate, a cross correlation analysis (PROC CORR; SAS Institute 1995) was
conducted with all possible predictor variables. Sets of independent predictor variables
were identified. From each set, variables were selected by forward, backward, and
stepwise elimination techniques. The model that most correctly related predicted and
observed responses was chosen as the final model. The model was validated and isopleths
generated as described above. Models produced from climate matching and biological
modeling were generated separately thus providing independent assessments of the
potential for establishment.
66
Risk Assessment of Copitarsia spp.
APPENDIX V: DETAILED RESULTS OF CLIMEX ANALYSIS
Climate Matching Locations which were classified a priori as having established
populations of Copitarsia tended to be more climatically similar to one of the 12 target
locations than locations classified as not having Copitarsia (Table 19). Locations with
Copitarsia were different from areas without Copitarsia in terms of temperature and a
combined measure of similarity (P < 0.01), but not in terms of precipitation (P > 0.05;
Table 19). Locations without Copitarsia were >40% similar to a Copitarsia infested site
(Table 19). The western half of South America, the southern half of Mexico, and the west
coast of the U.S. were 70% similar to a Copitarsia infested site based on minimum air
temperatures (Fig 4, A). Similarity based on precipitation was 70% throughout North and
South America (Fig. 4, B). Overall climatic similarity was greatest in southern South
America, Colombia, the Yucatan peninsula, and coastal California and Oregon (Fig. 4, C).
67
USDA-APHIS
Table 19: Climatic similarity of locations with or without Copitarsia to one of 12 target
locations with an established population of the pest.
Similarity of:
Maximum air
temperature
Minimum air
temperature
Precipitation
Precipitation
Pattern
Combined
With Copitarsia (n=41)
Mean ± SEM
Range
76.9 ± 2.9
52.6 - 100
Without Copitarsia (n=55)
Mean ± SEM
Range
40.1 ± 2.5
5.6 - 89.9
(t, P)
(-10.4, <0.01)
78.8 ± 2.0
54.2 - 100
37.7 ± 2.6
6.5 - 83.3
(-12.5, <0.01)
93.5 ± 1.0
95.4 ± 0.5
76.3 - 100
89.1 - 100
92.1 ± 0.8
96.4 ± 0.2
75.8 - 99.9
92.3 - 97.9
(-1.1, 0.28)
(1.9, 0.07)
68.8 ± 2.9
35.9 - 100
45.7 ± 1.5
21.7 - 78.7
(-7.1, <0.01)
Presence or absence of Copitarsia was determined prior to any climatic analysis.
Logistic regression indicated that the probability of Copitarsia establishment was most
closely associated with indices of climatic similarity based on minimum temperature and
precipitation, respectively (Table 20). At a threshold probability of 0.5 (i.e., establishment
is judged not to occur if the probability is < 0.5), the model correctly classified 89.6% of
all locations based on the presence or absence of Copitarsia. The model correctly
identified 90.2% of locations known to have Copitarsia and 89.1% of locations judged
inhospitable for Copitarsia. The model made incorrect predictions (i.e., gave false
negatives) for 7.5% of the locations where Copitarsia is known to exist. The model also
incorrectly predicted that Copitarsia would become established at 14% of the locations
judged inhospitable for the pest.
68
Risk Assessment of Copitarsia spp.
Table 20: Parameters for two logistic regression models used to relate the probability of
Copitarsia establishment to Climex indices from climate matching or biological models
Predictor
Coefficient SEM
Wald 2 (df=1) P
Climate Matching
Constant (b)
-23.2526
6.8094 11.7
0.0006
Minimum Temperature (X1)
0.1571
0.0337 21.7
0.0001
Precipitation (X2)
0.1470
0.0616 5.7
0.0170
Biological Modeling
Constant (b)
Cold Stress (X1)
2.5711
-0.7420
The final model follows: PEstablishm ent  
0.6001
0.194
1
18.4
14.7
0.0001
0.0001
. Predictors from climate
1  e  (b  m1 X 1 m 2 X 2 )
matching are Climex’s indices of climatic similarity to a Copitarsia-infested site based on
minimum temperature and precipitation, respectively. The predictor from biological
modeling is Climex’s index of cold stress.
When the logistic regression model was applied to a validation data set, the model
correctly classified 94.5% of all locations. The model correctly identified 93.9% of
locations where Copitarsia is known to exist and 95.0% of the locations where Copitarsia
was judged not to exist. The model had a false positive rate of 6% and a false negative rate
of 5%.
When the logistic regression model was applied to all 439 locations in the Western
Hemisphere, the model predicted that the probability of establishment in South America
was greatest (90%) in Peru, Chile, and southern Argentina (Fig. 5). In general, the
probability of establishment was 50% in much of the continent. The main exception
occurred in Brazil where the probability of establishment varied between 10 - 50% in a
significant portion of the country. The eastern half of the continent was generally less
likely to support the establishment of Copitarsia than the western half.
In North America, the probability of establishment was greatest (90%) in southern
Mexico, California, Oregon, Washington, and British Columbia (Fig. 5). Although the
probability was less than 50% for much of the continent, pockets existed where the
establishment probability was 50% in Idaho, Nevada, New Mexico, Texas, Oklahoma,
Arkansas, Missouri, Indiana, Illinois, Kentucky, Tennessee, Georgia, South Carolina,
North Carolina, Virginia, West Virginia, Maryland, Pennsylvania, New Jersey, and
Florida.
Biological modeling By iteratively adjusting Climex parameters until a satisfactory “fit”
of the geographic distribution of Copitarsia in Chile was achieved, we estimated that
Copitarsia could withstand temperatures from 0 to 30°C but required temperatures
between 4 and 28°C for populations to grow (Table 17). The species also required soils to
be between 25 - 200% of moisture holding capacity for populations to grow. However,
mortality was predicted to increase substantially as moisture exceeded 160% of a soil’s
holding capacity or dropped below 5% of holding capacity (Table 17). The rate of
69
USDA-APHIS
mortality due to drought was more than 10-times greater than for excessive moisture, cold
or heat.
When the parameter set was used to describe the influence of climate on population
dynamics throughout the Western Hemisphere, Climex estimated that 4500 - 9100 degree
days (base 4°C) would be accrued from ~40°S latitude to ~35°N latitude (Fig. 6, A). In
much of the remainder of the US, Copitarsia was likely to accumulate 2250-4500 degree
days. In most of Canada, 1125-2250 degree days would be accumulated.
Correspondingly, cold stress was greatest in Canada and the northern US where stress
indices ranged from 10 - 100% (Fig. 6, B). In the southeastern US, Mexico, Central
America, and South America values for the cold stress index ranged from 0 - 0.6%. The
exception to this trend occurred at La Quiaca, Argentina where the cold stress index was
6%. Climex integrated the cold stress index with a growth index and indices for heat
stress, drought stress, and moisture stress (data not shown) to produce an Ecoclimatic
index. The Ecoclimatic index has been used as an indicator of overall climatic suitability.
The greatest values for the Ecoclimatic index occurred in Paraguay and Colombia (Fig. 6,
C). Values for the index were greater than 10% throughout South America, with the
exception of the northeastern quarter of the continent. In the US, Washington, Oregon,
California, and most states east of the Rocky Mountains had EI values greater than 10%.
The Ecoclimatic Index was greater in locations with established populations of Copitarsia
than at locations pre-judged to be unsuitable for the species (Table 21). Similarly, the
number of accumulated degree days was greater where Copitarsia was already known to
be established. Cold stress was greater where Copitarsia was pre-judged not to exist. In
contrast, indices of heat stress and drought stress were greater where Copitarsia is known
to be established (Table 19). On average, neither of these indices exceeded 10%.
Table 21: Climex indices of population growth or environmental stress for Copitarsia at
locations with or without Copitarsia.
Index
With Copitarsia (n=38)
Without Copitarsia (n=53)
Mean ± SEM
Range
Mean ± SEM
Range
(t, P)
Degree days
5309 ± 273.3 1745 - 8587
1566 ± 61.5
408 - 2370 (-13.4, <0.01)
Temperature
46.7 ± 5.3
0 - 100
37.7 ± 1.2
15.9 - 58.8
(-1.7, 0.11)
Index
Moisture
63.2 ± 2.9
44.9 - 99.7
90.1 ± 1.6
59.9 - 100
(8.14, <0.01)
Index
Cold Stress
0.25 ± 0.17
0 - 6.1
21.0 ± 2.1
0 - 65.0
(10.1, <0.01)
Heat Stress
8.8 ± 3.8
0 - 95.1
0±0
0-0
(-2.3, 0.03)
Drought
0.2 ± 0.1
0 - 1.7
0±0
0-0
(-3.1, <0.01)
Stress
Wet Stress
<0.0 ± <0.0
0 - 0.2
<0.0 ± <0.0
0 - 0.1
(-1.0, 0.33)
Ecoclimatic
24.5 ± 3.9
0 - 98.3
4.5 ± 1.7
0 - 58.8
(-4.7, <0.01)
Index
Logistic regression indicated that the probability of Copitarsia establishment was most
closely associated with an index of cold stress (Table 20). At a threshold probability of
0.5, the model correctly classified 94.5% of all locations based on the presence or absence
70
Risk Assessment of Copitarsia spp.
of Copitarsia. The model correctly identified 97.4% of locations known to have
Copitarsia, and 92.5% of locations judged inhospitable for the pest. The model incorrectly
predicted that Copitarsia would become established at 9.8% of the locations judged
inhospitable for the pest. The model also incorrectly predicted that establishment would
not occur at 2% of the locations where Copitarsia is known to exist.
When the logistic regression model was applied to a validation data set, the model
correctly classified 94.8% of all locations. The model correctly identified 100% of
locations where Copitarsia is known to exist and 90.2% of the locations where Copitarsia
was judged not to exist. The model had a false positive rate of 10.0% and a false negative
rate of 0%.
When applied to the entire Western Hemisphere, the logistic regression model suggested
that the probability of establishment was 90% throughout most of South America (Fig.7).
The probability of establishment declined as proximity to La Quiaca, Argentina increased.
The probability of establishment remained 90% throughout Central America, through
most of Mexico, across the southeastern US, and into California, Oregon, Washington, and
British Columbia. The probability of establishment was 50% in 36 states. Only the states
of Alaska, Montana, Wyoming, Utah, Colorado, Nebraska, South Dakota, North Dakota,
Minnesota, Iowa, Wisconsin, Vermont, New Hampshire and Maine were unlikely to
support establishment of Copitarsia.
71
USDA-APHIS
APPENDIX VI: PRODUCTION OF INDIVIDUAL CROPS BY COUNTY WITHIN
THE UNITED STATES
72
Risk Assessment of Copitarsia spp.
73
USDA-APHIS
74
Risk Assessment of Copitarsia spp.
75
APPENDIX VII: NATURAL ENEMIES ATTACKING COPITARSIA IN ITS NATIVE RANGE
Type
Order
Family
Genus
Species
Bacteria
Bacillus
thuringiensis
Stage
attacked
larva
Fungus
Beauveria
bassiana
larva
Fungus
Entomophthora sphaerosperma
Insect
Coleoptera
Carabidae
Insect
Coleoptera Cocuineclidae
larva
Hylithus
spp.
Eriopis
conexa
egg
Country
Chile
Peru
Crop
Comments
Brassica epizootics of this fungus reached
nearly 80% of the larvae infested
after summer rains.
potato Applications of pesticides interfered
with activity of PA. In Dec-Apr
they did not recover
76
Insect
Diptera
Tachinidae
Archytas
scutellatus
larva
Chile
Insect
Diptera
Tachinidae
Archytas
scutellatus
larva
Chile
Insect
Diptera
Tachinidae
Archytas
scutellatus
larva
Chile
Insect
Diptera
Tachinidae
Dolichostoma
arequipae
larva
Peru
Insect
Diptera
Tachinidae
Euphorocera
peruviana
larva
Peru
Insect
Diptera
Tachinidae
Gonia
spp.
Insect
Diptera
Tachinidae
Incamyia
chilensis
Peru
larva
Chile
tomato low parasitism attributed to
intensive use of toxic
agrochemicals.
broad bean
Reference
(Lopez-A.
1996a)
(Lopez-A.
1996a)
(Aruta-M et al.
1974)
(Sanchez-V.
and MaitaFranco 1987)
(Lopez-A.
1996a)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
peas
(Lamborot et al.
1995)
unknown
(Alcala-C
1978a)
unknown
(Alcala-C
1978a)
Potato
(Sanchez-V.
and MaitaFranco 1987)
onion parasitizes a number of lepidoptera (Lamborot et al.
of agricultural importance in
1995)
Argentina and Uruguay, and
parasitizes noctuids in diverse crops
Type
Order
Family
Genus
Species
Stage
attacked
Country
Crop
77
Insect
Diptera Tachinidae
Incamyia
chilensis
larva
Chile
tomato
Insect
Diptera Tachinidae
Incamyia
chilensis
larva
Chile
broad bean
Insect
Diptera Tachinidae
Incamyia
chilensis
larva
Chile
peas
Insect
Diptera Tachinidae
Incamyia
species
larva
Peru
unknown
Insect
Diptera Tachinidae
Patelloa
similis
larva
Peru
unknown
Insect
Diptera Tachinidae
Patelloa
spp.
Peru
Potato
Insect
Diptera Tachinidae
Peleteria
robusta
Peru
unknown
Insect
Diptera Tachinidae
Peru
Potato
Peru
unknown
Peru
Potato
larva
Comments
Insect
Prosopochaet
a
setosa
Prosopochaet
Diptera Tachinidae
a
setosa
Insect
Diptera Tachinidae
Trichophorop
sis
spp.
Insect
Diptera Tachinidae
Winthemia
ignobilis
larva
Chile
broad bean
Insect
Diptera Tachinidae
Winthemia
species
larva
Peru
unknown
Insect
Diptera Tachinidae
Winthemia
spp.
Peru
Potato
Chile
5-6 species where there is intense
and continuous use of high dosages
of residual insecticides - in a
contiguous valley with lower
inseticide use, 15+ Tachinid
alfalfa species.
Insect
Diptera Tachinidae
larva
Reference
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Alcala-C
1978a)
(Alcala-C
1978a)
(Leyva-O. and
Sanchez-V.
1993)
(Alcala-C
1978a)
(Sanchez-V.
and MaitaFranco 1987)
(Alcala-C
1978a)
(Sanchez-V.
and MaitaFranco 1987)
(Lamborot et al.
1995)
(Alcala-C
1978a)
(Leyva-O. and
Sanchez-V.
1993)
(Cortes 1976)
Family
Genus
Species
Stage
attacked
Country
Insect Hymenoptera
Aphelinidae
Encarsia
portreri
egg
Chile
onion
Insect Hymenoptera
Aphelinidae
Encarsia
portreri
egg
Chile
Insect Hymenoptera
Aphelinidae
Encarsia
portreri
egg
Chile
eggplant
broad
bean
Insect Hymenoptera
Braconidae
Apanteles
spp.
larva
Insect Hymenoptera
Braconidae
Apanteles
spp.
Peru
Potato
Insect Hymenoptera
Braconidae
Apanteles
spp.
Peru
Potato
Insect Hymenoptera
Braconidae
Bracon
spp.
Peru
Potato
Type
Order
Crop
78
Insect Hymenoptera Ichneumonidae
Campolitis
sonorensis
Chile
Insect Hymenoptera Ichneumonidae
Netelia
gerlingi
larva
Chile
Insect Hymenoptera Ichneumonidae
Netelia
gerlingi
larva
Chile
Insect Hymenoptera Ichneumonidae
Insect Hymenoptera Ichneumonidae
Netelia
Thymebatis
gerlingi
hichinsi
larva
Chile
Chile
Insect Hymenoptera Ichneumonidae
Thymebatis
spp.
Peru
Insect Hymenoptera Ichneumonidae
Thymebatis
spp.
Peru
Comments
References
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lopez-A.
1996a)
(Leyva-O. and
Sanchez-V.
1993)
(Sanchez-V.
and MaitaFranco 1987)
(Sanchez-V.
and MaitaFranco 1987)
a new species for Chile - parasitized
up to 85%. Found in USA parasitizing
Spodoptera frugiperda, Heliothis
(Machuca et al.
unknown virescens, and Heliothis zea.
1989a)
(Lamborot et al.
onion
world-wide distribution
1995)
broad
(Lamborot et al.
bean
1995)
(Lamborot et al.
peas
1995)
alfalfa
(Porter 1980)
(Leyva-O. and
Sanchez-V.
Potato
1993)
(Sanchez-V.
and MaitaPotato
Franco 1987)
Type
Order
Family
Genus
Species
Stage
attacked
Country
Comments
Mass reared and released for
unknown establishment
swiss Introduced into Chile by mass
chard rearing
79
Insect
Hymenoptera Trichogrammatidae Trichogramma brasiliensis
Insect
Hymenoptera Trichogrammatidae Trichogramma minutum
egg
Chile
Insect
Hymenoptera Trichogrammatidae Trichogramma minutum
egg
Chile
tomato
Insect
Hymenoptera Trichogrammatidae Trichogramma minutum
egg
Chile
broad bean
Insect
Hymenoptera Trichogrammatidae Trichogramma minutum
egg
Chile
onion
Insect
Hymenoptera Trichogrammatidae Trichogramma
spp.
egg
Colombia
Potato
spp.
egg/larva
Insect
Neuroptera
Chrysopidaae
Chrysopa
Chile
Crop
References
(Loo-P and
Aguilera 1983)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lamborot et al.
1995)
(Lopez-A.
1996a)
(Lopez-A.
1996a)
USDA-APHIS
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