Predicting insect distributions from climate and habitat data
Article in BioControl · December 2008
DOI: 10.1007/s10526-007-9143-8
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Predicting insect distributions from climate and habitat data
Christian UlrichsÆKeith R. Hopper
Received: 13 July 2007 / Accepted: 8 November 2007 / Published online: 27 November 2007 ÓInternational Organization for Biological Control (IOBC) 2007
Abstract Knowing the effects of climate and habitat on the distributions of insect pests and their natural enemy would help target the search for natural enemies, increase establishment of intentional introductions, improve risk assessment for accidental intro- ductions and the effects of climate change. Most existing methods used to predict geographical distributions of insects either involve subjective comparisons of climate or require data concerning insect responses to climate. Here we have used geographical distributions of insects to develop statistical models for the effects of climate and habitat on these distributions. We tested this approach using six insect pests found in the United States:Ostrinia nubilalis(European corn borer),Diuraphis noxia(Russian wheat aphid), Helicoverpa zea (Corn earworm), Leptinotarsa decemlineata (Colorado potato beetle), Solenopsis invicta(Red imported fire ant), andConotrachelus nenuphar(Plum curculio).
By randomly separating the data into model-building and test sets, we were able to esti- mate prediction accuracy. For each species, a unique combination of predictor variables was identified. The models correctly predicted presence for more than 92% of the data on each insect species. The models correctly predicted absence for 59% to 77% of the data on five of six species. Absence predictions were poor for H. zea (21% correct), because distribution data were limited and inaccurate. Predictions of insect absence were more difficult because absence data were less abundant and perhaps less reliable. This approach offers potential for the analysis of existing data to produce predictions about insect establishment. However, accurate prediction depends heavily on data quality, and in particular, more data are needed from locations where insects are sampled but not found.
Keywords Climate matchingPest risk predictionBiological control introductions Insect distributionInsect habitatAnalogous climateBiosecurity
C. UlrichsK. R. Hopper
Beneficial Insect Introduction Research, USDA-ARS, Newark, DE, USA Present Address:
C. Ulrichs (&)
Institute for Horticultural Science, Section Urban Horticulture, Humboldt-Universita¨t zu Berlin, Lentzeallee 55/57, 14195 Berlin, Germany
e-mail: [email protected] DOI 10.1007/s10526-007-9143-8
Introduction
Biological control through the introduction of natural enemies has been practiced for over 100 years, but much remains to be discovered about the factors determining success or failure of introductions. About two thirds of the predatory and parasitic insects introduced have failed to establish (Hall and Ehler1979; Stiling 1990), and only about half of the introductions that establish against arthropod pests provide some level of economic con- trol. These failures have often been attributed to a lack of match between climates of source and target regions (Clausen1978; Stiling1993; Goolsby et al.2005). This makes sense because climate affects the population dynamics of insects and is therefore a useful predictor of potential establishment and spread in new areas (Cammell and Knight1992;
Gevrey and Worner2006; Hance et al.2007). Furthermore, climate has a large effect on the distribution of host plants (Guo et al. 2006) and thereby indirectly influences the distribution of phytophagous insects and their natural enemies (Kiritani2006; Tuda et al.
2006). Thus a widely accepted principle in biological control is that natural enemies should be collected from climates that match as closely as possible the environment into which they will be introduced (Thompson and Parker1928; Messenger and van den Bosch1971;
Stiling1993; Sutherst2003; Hoelmer and Kirk2005). In a review of factors correlated with establishment of introduced parasitoids, Stiling (1990) found that tropical parasitoids introduced into temperate areas established with lower frequency (24%) than when introduced into tropical areas (35%), but parasitoids from temperate areas established with about equal frequency in tropical (35%) and temperate areas (38%). However, these comparisons are rather crude and not useful for most biological control programs. Fur- thermore, climate is only one among several factors likely to affect establishment and efficacy. Samways et al. (1999) found that climate match alone often did not predict establishment of Chilocorus spp. introduced into various regions for biological control.
Nonetheless, the usefulness of climate matching to improve the success rate of introduc- tions into new environments is well established (DeBach 1964; Clausen 1978; Goolsby et al.2005; Hoelmer and Kirk2005). However, recommendations about how to actually match climates are rare and often obscure (Hopper1996). Because the effects of climate and habitat vary from species to species, precise effects on any individual species are difficult to predict (Aspinall and Matthews 1994). Knowing the effect of climate and habitat on pest distribution would help target the search for natural enemies to introduce against exotic pests. Furthermore, risk of non-target impacts could be reduced by improved screening habitat and climate ranges, together with a better understanding of the mecha- nisms underlying these ranges (Hopper1998).
Climate/habitat modeling is being increasingly used in conservation biology and ecology (Leathwick 1998; Scott et al. 2002), estimating the past and predicting future effects of global climate change (Mourell and Ezcurra1996; Baker et al.2000; Rafoss and Saethre2003), and predicting and managing invasive species (Baker et al.2000; Hartley et al.2006). The potential distribution of an insect species may be predicted using various modeling techniques. Models based on physiological data for the target insect are referred to as mechanistic (Beerling et al. 1995) or ecophysiological models (Stephenson1998).
Expert systems, such as SPECIES (Spatial estimator of Climate Impacts on the Envelope of Species) (Pearson et al.2002) and CLIMEX (Young et al.1999; Kriticos and Randall 2001) use this approach, and provide forecasts of pest and disease population dynamics based on climate (Dentener et al. 2002). Predictions by SPECIES come from a neural network model, which integrates bioclimatic variables for predicting the distribution of species through the characterization of bioclimatic envelopes. The CLIMEX model
describes the response of a species to climate and can compare either locations or years.
CLIMEX uses a database of meteorological climate station data, stored as long-term monthly averages (1931–1960) based on data from the World Meteorological Organization (Sutherst et al. 2005). The match index is fairly simple and derived from comparing monthly rainfall and average maximum as well as minimum temperatures between two locations. In the newest version (version 3) additionally soil moisture and average tem- perature have been added as variables that can be included in the match index for match climates. Recent examples using CLIMEX include the forecast ofHelicoverpapopulations in Australia (Zalucki and Furlong 2005), a climate-model of the Red imported fire ant, Solenopsis invictaBuren for invasion of new regions, particularly in Oceania (Sutherst and Maywald 2005), a model for the geographical distribution of the Oriental fruit fly, Bactrocera dorsalis(Stephens et al.2007), and the search for new invasive pest species for New Zealand (Peacock and Worner2006). Both SPECIES and CLIMEX rely on data about physiological tolerances of each insect species to predict distribution. This approach has two limitations: (1) detailed data about physiological tolerances are not available for most species, and (2) if data are available, they are usually from the laboratory and do not reflect the complex environment outdoors. Additionally these models do not include other pre- dictor variables such as habitat data, which is likely to help in predicting geographical distribution. GARP (Genetic Algorithm for Rule-Set Prediction) is an expert-system approach to predictive modeling and can include various types of predictor variables (Stockwell and Peters1999). GARP has been used to predict species distributions (Elith and Burgmann2002; Lim et al.2002). But in some cases, the GARP model did not predict well and was outperformed by simple regression models (Stockman et al.2006).
Here we use the current distributions of insects, together with climate and habitat data, to generate statistical descriptions of the relationship between distribution on one hand and climate and habitat on the other. This approach can be used to predict species distribution.
The exercise of producing species range maps is not new (Rapoport1982) and in fact is common in biogeography (Hengeveld1990; Gaston2003). Typically ranges are delimi- nated using sample data and these may be either local abundance or simply presence and absence (Maurer 1994). A statistical approach avoids the limitations of mechanistic models. The underlying assumption is that an organism does not occur in an area from which it is not separated by physical barriers because environmental conditions are not favorable. The ability of species to disperse as a function of landscape has been described by Holt and Keitt (2000) and Fortin et al. (2005). Knowledge of species habitat and climate requirements can be used to obtain a probabilistic map of species occurrence based on logistic regression and predictive models (Guisan and Hofer2003; Sutherst2003; Loiselle et al.2003).
The approach requires data on climate, habitat, and insect distribution. At first we planned to use data on introduced natural enemies because the success/failure of introductions would provide data to test the models. However, few data are available concerning the distribution of natural enemies of pests in source regions. Our goal was to generate models to predict natural enemy establishment and efficacy in the region where a target pest occurs and to assess the likelihood of establishment of new pests.
Because distribution data are more abundant and detailed for pest insects, we developed and tested our model using data on pests. The best means of objectively assessing model performance is to use an independent set of locality records (Fielding and Bell 1997), therefore we randomly divided the insect distribution data into separate sets for calibration and for testing. For this study, we selected six insect pests:Ostrinia nubilalis (Hu¨bner) (European corn borer), Diuraphis noxia (Mordvilko) (Russian wheat aphid),
Helicoverpa zea (Boddie) (Corn earworm), Leptinotarsa decemlineata(Say) (Colorado potato beetle), Solenopsis invicta Buren (Red imported fire ant), and Conotrachelus nenuphar (Herbst) (Plum curculio). We chose these species because they are major pests in the United States for which we expected to find relatively accurate and extensive distribution data. Although some are introduced or have undergone historical range expansions, we considered their current distributions to be relatively stable. It is a basic postulate with this type of modeling, that within a time frame of interest and considering a certain spatial scale, species distributions are in equilibrium with their surrounding environment (Guisan and Theurillat 2000). Using insect distribution, cli- mate, and habitat data, we developed and tested statistical predictions for presence versus absence of these insects.
Materials and methods
Insect distribution data
Insect distribution data were from the National Agricultural Pest Information System (NAPIS). NAPIS provides data management for plant pest survey data, gathered as part of the Cooperative Agricultural Pest Survey (CAPS) in the United States. Distributions were recorded during 1960–2001 as presence or absence of a species by county. For our study, if a species was recorded in a county at any point during this period, we con- sidered it present. If a species was ever recorded as absent and never recorded as present, we considered it absent. Data for counties with no records of presence or absence for a species were considered missing values. NAPIS currently has data on more than 3,800 species, including insects, pathogens, weeds, and biological control agents. But for most species, only limited data are available. We selected six species for which we expected there to be relatively abundant data and for which we could assume they had reached their maximum distribution because they were native, introduced long ago, or their distribution has not changed substantially over the last 10 years. A brief history of their distribution follows.Ostrinia nubilalis, the European corn borer, a major pest of corn in the US, was introduced from Europe in the early 1900s (Metcalf and Metcalf1993) and rapidly spread across North America. Because of the great economic importance of this pest, crop losses and control measures for this pest cost US farmers over $1 billion annually (Mason et al. 1996), surveys have been conducted in 38 states of the contig- uous US.Diuraphis noxia, the Russian wheat aphid, was first reported in the US in 1986 and has become a major pest of wheat and barley, causing over $850 million in direct and indirect losses from 1987 to 1992 (Brooks et al. 1994). Because of its economic impact, it has been closely monitored, and there was no substantial change in distri- bution over the past decade. Helicoverpa zeais native to North America, and surveys have shown that it is present in much of the US. This insect has a wide host range, including such economically important crops as corn, cotton, and tomato (Capinera 2001). However, absence data for this species are crucially lacking in the database, as is often the case for insect distributions (Anderson et al.2002).Leptinotarsa decemlineata is probably one of the best known insects in the US today. It is native to North America and was first discovered in 1811 in the Rocky Mountains. However, as European immigrants started planting new crops, especially potatoes, the insect migrated to this new and much more available food source. The insect is today found throughout the contiguous US except for California and Nevada (Capinera 2001). Despite this wide
distribution, presence and absence data are only recorded sporadically throughout the country. Solenopsis invictawas introduced into the United States during the 1920s. By the first survey in 1953, it had invaded 102 counties in 10 states (Culpepper 1953).
Today,S. invictahas spread throughout the southeastern US.Conotrachelus nenupharis native to North America and is the most important insect pest of peaches in the southeastern United States (Horton and Ellis1989). Throughout its habitat,C. nenuphar uses a broad range of hosts, including plants in the Rosaceae (Maier 1990) and in the Ericaceae (Polavarapu et al.2004).
Climate and habitat data
We used several climatic variables, summarizing data by county in different ways, depending on the variable (Table1). Climate data were from the PRISM (Parameter- elevation Regressions on Independent Slopes Model) climate mapping program developed at Spatial Climate Analysis Service (SCAS), Oregon State University, and were provided as spatial climate data sets for the period 1960–1990. PRISM is an expert system that uses point data and a digital elevation model (DEM) to generate gridded estimates of climate parameters (Daly et al.1994). The resolution for the gridded data was either 1.25 arc-min (relative humidity, temperature extremes for the period of record) or 2.5 arc-min (monthly mean, minimum, maximum temperature and precipitation). The data for mean, maximum, and minimum temperature as well as precipitation were downloaded from the USDA Natural Resources Conservation Service (NRCS) webpage.
Landcover data were from the Multi-resolution Land Characteristics Consortium downloaded as geo-coded images from the Environmental Protection Agency (EPA) website (http://www.epa.gov/mrlc/nlcd.html). We used the pixel count of each unique pixel combination (county code versus landcover code) to calculate percent landcovers for each county for several aggregated landcover types (Table2).
Table 1 Climate variables in the model for the period 1960–1990
Variable Spatial scale Statistic Unit
Relative humidity County mean Monthly mean %
Temperature County extreme Absolute maximum for month °C
Temperature County extreme Mean of maxima for month °C
Temperature County extreme Mean monthly maximum °C
Temperature County mean Mean monthly maximum °C
Temperature County mean Monthly mean °C
Temperature County mean Mean monthly minimum °C
Temperature County extreme Mean monthly minimum °C
Temperature County extreme Mean of minima for month °C
Temperature County extreme Absolute minimum for month °C
Precipitation County mean Mean of maximal daily precipitation for month mm
Precipitation County mean Monthly mean mm
Precipitation County mean Absolute maximum daily precipitation mm
GIS data processing
The geographic area used for this study was the contiguous United States. Climate and landcover data were received in a gridded format with various resolutions. Insect distri- bution data were on a county basis. Insect distribution data were geo-referenced by NAPIS with a Federal Information Processing Standard (FIPS) code, a five digit number code representing the counties of the 50 States. Using a county layer with FIPS codes provided by ESRI (ESRI Data & Maps Media Kit, CD1, version 2002), we converted climate and landcover data to a county basis with the geo-statistical analyst in ArcInfo version 8.2.
Statistical analyses
Assessing the predictive ability of a model is crucial, and recent publications have pro- vided guidelines for evaluating predictive models (Pearce and Ferrier2000; Manel et al.
2001). Manel et al. (2001), amongst others, using independent data for model evaluation.
To provide separate data sets for model building versus testing, we divided the data for each insect into two random subsets. Because the insect distribution data were unbalanced with much more presence than absence data, we separated the presence records and the absence records for a species, randomized them separately, and then recombined them for analysis into model-building and testing subsets. This is a common strategy for evaluating model quality (Fielding 2002). However, by excluding part of the data set from the
Table 2 Definitions of landcover types in the model Landcover
type
Definition Corresponding EPA
landcover class Water All areas of open water and all areas with year-long ice or snow. 11, 12 Wetland Areas with 25–100% forest or shrubland or with 75–100 perennial
herbaceous vegetation and the soil or substrate is periodically saturated with or covered with water.
91, 92
Developed Areas with a mixture of construction and vegetation. Construction 30–100% of cover; vegetation up to 70% of the cover.
21, 22, 23 Barren Areas\25% vegetative cover. Areas of extractive mining activities
with significant surface expression; perennially barren areas of bedrock, desert pavement, scarps, slides, volcanic material, glacial debris, beaches, and other accumulations of earthen material.
31, 32, 33
Shrubland Areas with 25–100% shrubland. Shrub cover is generally greater than 25% when tree cover is less than 25%. Shrub cover may be less than 25% in cases when the cover of other life forms is less than 25% and shrubs cover exceeds the cover of the other life forms.
51
Forest Areas dominated by trees, including orchards, vineyards, and other areas planted or maintained for the production of fruits, nuts, berries, or ornamentals.
41, 42, 43, 61, 91
Herbaceous Areas dominated by upland grasses, legumes, and forbs. These areas are also used for the production of crops, such as corn, soybeans, vegetables, tobacco, wheat, barley, oats, rice, and cotton. Examples include parks, lawns, golf courses, airport grasses, and industrial site grasses.
71, 81, 82, 83, 84, 85, 92
model-building stage, the algorithm cannot take advantage of all known locality records.
The second problem associated with this kind of model evaluation procedure is that we cannot fully exclude any auto-correlation effects between data used for modeling and for model evaluating. It would be more rigorous to use one region to parameterize the model, and then test it independently in another. Here the datasets (county sample size is given in Figs.1and2) we have were too small to follow this approach.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
C. nenuphar L.
decemlineata
D. noxia O. nubilalis H. zea S. invicta
Incorrect Correct
12421 914 3195 9106 8427 5435 sample size
Fig. 1 Percent correct and incorrect predictions of presence of each species
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
C. nenuphar L.
decemlineata
D. noxia O. nubilalis H. zea S. invicta
Incorrect Correct
144 49 1300 456 192 1044 sample size
Fig. 2 Percent correct and incorrect predictions of absence of each species
Using the model-building data, we used the stepwise discriminant function procedure in SAS version 8 (SAS Institute Inc. 1989) to develop models for each insect species to predict presence versus absence by county from climate and landcover data. In stepwise discriminant function analysis, independent variables are chosen stepwise to enter or leave a discriminant function, depending on the probability of the appropriate F-ratio, until no further variables are significant with that probability. We used a threshold probability of 0.05 to select variables to enter or leave the model. Each resulting model was then tested for accuracy of predicting presence versus absence with the appropriate test data set.
Results and discussion
Determining the key environmental factors that affect geographical distribution of certain insect species can lead to improved success of introduced natural enemies, as well as predictions about risk of accidental pest establishment. The ecological causality of various types of predictors commonly used to predict plant or animal distribution has been dis- cussed by Austin (1980). He divides these predictors in three categories: (1) indirect predictors with no causal relationship with the target organism’s physiology, (2) direct predictors, having a physiological effect on the organism, and (3) resource predictors, which constitute a resource to be assimilated by the organism. More recently, Austin (2002) proposed a new terminology where all predictor types are arranged along a gradient from distal (indirect) to proximal (direct, resource). The predictors we have used in our approach range from proximal (climate factors) to more distal (landcover data). Therefore, we found for each species, a unique combination of independent variables that explained significant variation in presence versus absence (Table3). Relative humidity was included in the models for three of the six species. Some measure of temperature was important in the models of all species exceptL. decemlineata, for which the percentage of shrubland was the most important single variable, explaining over 47% of the variation. Shrubland was also important in the models ofC. nenupharandO. nubilalis. The high explanatory power of landcover was quite surprising since these species are polyphagous, and the landcover categories were broad.
The models were able to correctly predict the presence of each species where it was indeed found with over 93% accuracy (Fig.1). A high frequency of correct predictions for presence is not surprising for most species because most of the distribution data recorded presence. Nonetheless, the percentage of correct predictions of presence exceeds that expected by chance (50% overall) for all species. The frequency of correct predictions of presence was striking for D. noxiaandS. invicta, where a higher percentage of the dis- tribution data recorded absence (33% and 23% respectively). If we assume that species were truly absent in those counties for which the models incorrectly predicted presence, there are two explanations for these incorrect predictions: (1) the environment is indeed unfavorable and the model is wrong, or (2) the insect never reached the county. We tried to eliminate the second possibility by selecting only insects which might have reached their maximal distribution. However, these counties may represent areas to which future spread is possible. On the other hand, absence data may be inaccurate because of survey errors, misidentification, and seasonal migration (Fielding and Bell 1997). Thus, we may have scored a prediction of presence as incorrect when it was actually correct and the distri- bution data were wrong.
The models were able to correctly predict the absence of each species where it was indeed absent with 60 to 77% accuracy for five of the six species (Fig.2). Predicting
Table3R2forclimateandlandcovervariables VariablesC.nenupharL.decemlineataD.noxiaO.nubilalisH.zeaS.invicta Relativehumidity Monthlymean–*0.0052–0.00560.0049– Temperature Absolutemaximumformonth––0.01980.00330.00880.0025 Meanofmaximaformonth0.0020–0.03710.00360.01210.0373 Meanmonthlymaximum–––0.0026–0.0232 Meanmonthlymaximum–––0.0188–0.0120 Monthlymean0.0015–0.0126–0.00210.0023 Meanmonthlyminimum0.1666**–0.0115––0.0075 Meanmonthlyminimum0.0069–0.00350.03480.00140.0365 Meanofminimaformonth0.0355–0.00300.00190.00080.0052 Absoluteminimumformonth0.0006–0.00810.00070.00100.0926 Precipitation Meanofmaximaldailyprecipitationformonth0.0134–0.04410.00820.00990.1935 Monthlymean0.0091––0.02030.00190.1740 Absolutemaximumdailyprecipitation0.0036–0.00590.0018–0.0012 Water(%countyarea)––0.0033––0.0024 Developed(%countyarea)0.0014––0.0007–0.0050 Barren(%countyarea)0.00550.0189–0.0433–0.0283 Shrubland(%countyarea)0.45720.47050.01850.38890.0006– Wetland(%countyarea)0.0006–0.1014–0.01530.0168 Forest(%countyarea)–0.00530.0078––0.0024 Herbaceous(%countyarea)0.0093–0.01030.01210.03260.0016 *Notsignificant;**Valuesforvariablesexplainingmorethan10%ofvariancegiveninbold
absence was more difficult because relatively few data were available concerning absence.
ForH. zea, only 5% of all data points recorded absence. Thus, we predicted absence when H. zeawas actually present for 79% of the counties. The distribution map forH. zeashows clearly that pest surveys were inaccurate and often based on political boundaries (Fig.3).
This resulted in presence reports for entire states such as Nevada and Utah, but no reports for neighboring states such as California and Wyoming. Thus survey data forH. zeawere very patchy and insufficient, with a crucial lack of absence data for this species. The distribution data forS. invictawere much more accurate (Fig.4), and the models predicted absence with 65% accuracy. The good presence/absence data forS. invictaare most likely available because this species is a quarantine pest and therefore often subject to more extensive detection surveys than widely established insect pests. The geographic distri- bution of S. invicta covers a wide range of climatic conditions in the US (Fig.4). In addition to the southern area from Mexico to Virginia and inland to Texas and from southern Oklahoma to North Carolina, it persists in irrigated habitats in southern California (Callcott and Collins1996), west Texas, and New Mexico (MacKay and Fagerlund1997).
Soil moisture has been a limitation in western Texas and New Mexico (Stoker et al.1994;
MacKay and Fagerlund 1997). Therefore we assume that adding the parameter soil moisture as possible predictor would have resulted in a higher prediction accuracy.
The impacts of incorrect predictions are varied and asymmetrical. Four scenarios with different consequences are possible: (1) predicting a natural enemy will establish when it won’t may waste time and money, if introduction is attempted; (2) predicting a natural enemy wont establish when it will could have two consequences, wasted opportunity, if the introduction is not attempted, and unforseen exposure of non-target species, if the natural
Fig. 3 Helicoverpa zeadistribution in United States from 1960 to 2001
enemy establishes where it was not thought possible; (3) predicting establishment by a potential pest when it is unlikely may waste time and money on interdiction; (4) predicting that a region is not at risk from a pest, which subsequently invades, may result in much greater losses. Our models were very good at predicting presence but not as good at predicting absence. Thus they would greatly reduce the likelihood of scenarios (1) and (3).
However, the models had too great a likelihood of scenarios (2) and (4) and thus would need to be supplemented with other information.
The success of this statistical approach depends completely on the availability and quality of insect distribution data. Insect presence was predicted with a much higher accuracy than absence, in part because absence data were thin. Absence data are often not available (Margules and Austin 1994), and may be less reliable than presence data (Fielding and Bell 1997) for reasons discussed previously. Nonetheless, distribution data are being collected more often and are becoming more readily available.
Extrapolation is another problem with statistical models. Because statistical models are generated from data for specific region, they can be poor at predicting establishment in a new region. Such failures may have two explanations: (1) the understanding of factors affecting distribution generated from the old data is incomplete, or (2) the species evolves to adapt to the new environment. For this and other reasons, the use of bioclimatic envelopes for prediction of distributions has been questioned (Woodward and Berlin1997; Samways et al.1999; Lawton2000). However, predicted distributions based on climate may provide a framework for biological information about species (Rafoss and Saethre2003).
Incorrect predictions of presence and absence suggest that additional factors affect these species distributions. In particular, abundance of host plants, natural enemies, and
Fig. 4 Solenopsis invictadistribution within the contiguous United States from 1960 to 2001
competitors are not included in these models. However, this approach lends itself readily to incorporation of such factors if the data become available. Our results suggest that the approach of using discriminant function analysis or similar statistical methods (e.g., logistic regression) offers considerable potential for predicting presence versus absence of insect species. It can help to identify regions which are at risk for unintentional intro- ductions of specific pests, where intentionally introduced insects may flourish, and where climatically adapted natural enemies are likely to be found. However, this study included only selected climate and habitat variables. The modeling process could be developed further to link multiple layers of environmental, biological, and land-use information.
Acknowledgments We are grateful to Jim Pheasant of the National Agricultural Pest Information System (NAPIS) unit for giving us access to the extended US pest data-base.
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