quantitative ecology field-level validation of a climex ...quantitative ecology field-level...

QUANTITATIVE ECOLOGY

Field-Level Validation of a CLIMEX Model for Cactoblastis cactorum(Lepidoptera: Pyralidae) Using Estimated Larval Growth Rates

BENJAMIN C. LEGASPI, JR.1 AND JESUSA CRISOSTOMO LEGASPI2

USDAÐARS, CMAVE/Florida A&M UniversityÐCenter for Biological Control, 6383 Mahan Dr., Tallahassee FL 32308

Environ. Entomol. 39(2): 368Ð377 (2010); DOI: 10.1603/EN08248

ABSTRACT Invasive pests, such as the cactus moth, Cactoblastis cactorum (Berg) (Lepidoptera:Pyralidae), have not reached equilibrium distributions and present unique opportunities to validatemodels by comparing predicted distributions with eventual realized geographic ranges. A CLIMEXmodel was developed forC. cactorum. Model validation was attempted at the global scale by comparingworldwide distribution against known occurrence records and at the Þeld scale by comparingCLIMEX “growth indices” against Þeld measurements of larval growth. Globally, CLIMEX predictedlimited potential distribution in North America (from the Caribbean Islands to Florida, Texas, andMexico), Africa (South Africa and parts of the eastern coast), southern India, parts of Southeast Asia,and the northeastern coast of Australia. Actual records indicate the moth has been found in theCaribbean (Antigua, Barbuda, Montserrat Saint Kitts and Nevis, Cayman Islands, and U.S. VirginIslands), Cuba, Bahamas, Puerto Rico, southern Africa, Kenya, Mexico, and Australia. However, themodel did not predict that distribution would extend from India to the west into Pakistan. In the UnitedStates, comparison of the predicted and actual distribution patterns suggests that the moth may beclose to its predicted northern range along the Atlantic coast. Parts of Texas and most of Mexico maybe vulnerable to geographic range expansion of C. cactorum. Larval growth rates in the Þeld wereestimated by measuring differences in head capsules and body lengths of larval cohorts at weeklyintervals. Growth indices plotted against measures of larval growth rates compared poorly whenCLIMEX was run using the default historical weather data. CLIMEX predicted a single periodconducive to insect development, in contrast to the three generations observed in the Þeld. Only timeand more complete records will tell whether C. cactorum will extend its geographical distribution toregions predicted by the CLIMEX model. In terms of small scale temporal predictions, this studysuggests that CLIMEX indices may agree with Þeld-speciÞc population dynamics, provided anadequate metric for insect growth rate is used and weather data are location and time speciÞc.

KEY WORDS bioclimatic models, climate envelope, model validation, cactus moth, Cactoblastiscactorum

Bioclimatic models (also referred to as envelope, eco-logical niche, or species distribution models) are usedto predict potential distribution and population levelsof biological organisms based on known ecological andclimatic tolerances in their native habitats (Gullanand Cranston 2005, Heikkinen et al. 2006, Jeschke andStrayer 2008). These models have been criticized ontheoretical grounds (Davis et al. 1998a, 1998b; Lawton1998; Hodkinson 1999; Baker et al. 2000). Clearly,distribution of organisms is determined by factors

other than climate (Legaspi and Legaspi 2007a). Pear-son and Dawson (2003) cited three general noncli-matic factors that affect species distribution: (1) bioticinteractions such as competition and predation; (2)rapid local evolution, causing changes in geographicdistributions, with or without concurrent environ-mental change (Parmesan et al. 2005); and (3) speciesdispersal that uncouples dependence on local climateand allows populations to persist in suboptimal envi-ronments (Davis et al. 1998b). Despite criticismsagainst bioclimatic models, even critics accept thatthey may be useful as “Þrst approximations” (Pearsonand Dawson 2003) or “null models” of species distri-butions (Davis et al. 1998a, b,; Lawton 1998). They areuseful in identifyingkeyrelationshipsbetweenspeciesand factors governing their distribution and for pre-dicting potential effects of global climate change onbiodiversity (Heikkinen et al. 2006). In the absence ofappropriate data, climate matching may be the only

This article presents the results of research only. The use of trade,Þrm, or corporation names in this publication is for the informationand convenience of the reader. Such use does not constitute an ofÞcialendorsement or approval by the U.S. Department of Agriculture orthe Agricultural Research Service of any product or service to theexclusion of others that may be suitable.

1 This author is employed by the State of Florida; please contactthrough J.C.L.

2 Corresponding author, e-mail: [email protected].

viable option to predict species distributions (Baker etal. 2000).

Bioclimatic models created using CLIMEX (Suth-erst and Maywald 1985) (Hearne ScientiÞc Software,Melbourne, Australia) are typically developed in atwo-fold process. The initial phase of model parame-terization consists of replicating the known distribu-tion of the target species in its native habitat using dataon climatic conditions favorable to the target species,as well as stress factors detrimental to survival (Suth-erst et al. 2004). Afterward, the geographical area ofinterest is extended or a new area is chosen. CLIMEXhas an extensive publication history (Sutherst et al.2004) and has been used in studies on climate change(Sutherst 2004), quarantine pest risk (Sutherst andMaywald 1991), biological control (Goolsby et al.2005), biogeography (Samways et al. 1999), policy(Sutherst 1998), and education (CRS 2004). Despitethe multiple uses of CLIMEX and similar bioclimaticmodels, rigorous validation against independent datasets is insufÞcient, largely because opportunities fortesting are limited (Sutherst 1998, Sutherst and May-wald 2005).

The cactus moth, Cactoblastis cactorum (Berg)(Lepidoptera: Pyralidae), has a long history in bio-logical controlÑÞrst as a textbook example of suc-cessful classical biological control, and more recently,as a warning of the perils associated with unintentionalconsequences of biological control agents (Raghu andWalton 2007). Cactoblastis cactorum was importedfrom Argentina into Australia in 1926 to control inva-sive Opuntia cacti at a cost (adjusted for inßation)estimated to be $700M, which proved well justiÞedwhen the land was returned to agricultural produc-tivity within 5 yr (Raghu and Walton 2007). Morerecently, C. cactorum was Þrst documented in NorthAmerica in the Florida Keys in October 1989Ñitsarrival is a possible consequence of island-hoppingfrom release sites in the Caribbean (Habeck and Ben-nett 1990). Since then, the moth has progressivelyexpanded its geographical distribution (Hight et al.2002). Currently, distribution limits of the cactus mothare as far north as Bull Island, SC, on the Atlantic Coast(Simonson et al. 2005, Bloem et al. 2007) and as farwest as Mississippi and Louisiana along the Gulf Coast(Pollet 2009). The moth was also reported in August2006 on Isla Mujeres, a small island off the northeastcoast of the Yucatan peninsula in Mexico, possiblydispersed by winds and hurricanes or accidental trans-port through tourists orcommercial trade(HernandezBaeza 2006, Legaspi and Legaspi 2008, Pollet 2009).

The lifehistoryof thecactusmothwas studied in thelaboratory at constant temperatures of 18, 22, 26, 30,and 34�C (Legaspi and Legaspi 2007b). Total imma-ture development time from eggs to pupae was �180d at 18�C, 116 d at 22�C, and ranged from 65 to 72 d at26Ð34�C. Estimated lower developmental thresholdtemperature was 13.3�C. The highest reproductive val-ues were found at 30�C: net reproductive rate (R0),

gross reproductive rate (GRR), generation time (T),intrinsic rate of increase (r), Þnite rate of increase (�),and doubling time (DT) were 43.68 �/�, 44.02 �/�,

67.14 d, 0.0562 �/�/d, 1.058 �/�/d, and 12.33 d,respectively. Peak oviposition has been found at day3 of the adult female life (Legaspi et al. 2009b). InFlorida, C. cactorumwas found to undergo three gen-erations per year (Hight and Carpenter 2009), whichgenerally occurred in August to September, Octoberto April, and May to July (Legaspi et al. 2009a).

The cactus moth is known to attack 79 species ofprickly pear cactus; 51 endemic to Mexico, 9 endemicto the United States, and 19 common to both countries(Zimmermann et al. 2000). In the United States, C.cactorum threatens the cactus industry in Arizona,California, Nevada, New Mexico, and Texas, wherecacti are grown primarily as ornamentals. The mothhas been identiÞed as a signiÞcant threat to the valu-able cactus industry of Mexico (Perez-Sandi 2001,Vigueras and Portillo 2001), where �250,000 ha arecultivated to cactus, producing annual economic rev-enue of about $50 million (1990Ð1998) (Soberon et al.2001).There is no known effective control methodagainst C. cactorum, although signiÞcant research hasbeen conducted on the use of sterile insect technique(SIT) methods (Hight et al. 2005).

Here we developed and attempt to validate aCLIMEX model for C. cactorum at a global scale bycomparing predicted global distribution with knownoccurrence records and at a Þeld scale by comparinggrowth index values with measured larval growth ratesof Þeld cage specimens. We modeled predicted dis-tribution of an invasive pest that most likely has not yetattained its full geographic potential in North Amer-ica. C. cactorumwas chosen as the test insect not onlybecause it is invasive but also because it represents asigniÞcant potential economic pest to the cactus in-dustry in the southwestern United States (Irish 2001),as well as that of Mexico where the cactus industry isvital (Soberon et al. 2001).

Materials and Methods

CLIMEXTheory.The CLIMEX model (Sutherst etal. 2004) is based on the assumption that persistenceof a species in a speciÞc geographic location allowsinferences to be made regarding climatic conditions itcan tolerate. These inferences, which are based onactual distributions, allow predictions regarding po-tential distributions in space and time. The climaticrequirements of a target species are typically inferredfrom known geographical distributions, often its na-tive range or other areas where it is long established.After initial parameter estimation, laboratory andother published data may be used to Þne tune themodel. The Þnal values in a species parameter Þle arederived through an iterative process of comparing theknown and predicted distributions for the same re-gion. Afterward, the “Compare Locations” functionmay be used to graphically describe potential distri-bution in other independent areas. This function isprimarily used to approximate potential distribution ofa species as determined only by climatic variables.

CLIMEX uses several indices (scaled from 0 to 1)and two constraints to calculate potential species sur-

April 2010 LEGASPI AND LEGASPI: VALIDATION OF CLIMEX MODEL FOR CACTUS MOTH 369

vival at agiven location.Foreachweek, agrowth index(GI) is calculated based on factors favorable to insectgrowth (e.g., temperature, moisture), predominatingduring favorable seasons. Each index is estimated us-ing a lookup table deÞned by parameter values. Forexample, the temperature index (TI), is determinedby four parameters (DV0, DV1, DV2, and DV3): TI �0.0 for temperatures � � DV0; TI � 1.0 for temper-atures between DV1 and DV2; and TI � 0.0 for tem-peratures � � DV3. TI values within temperatureranges DV0 to DV1 and DV2 to DV3 are calculated bylinear interpolation. Alternatively, stress indices re-ßect factors that limit growth and survival (heat, cold,wet, and dry stresses and their interactions), predom-inating during unfavorable seasons. Parameters for agiven species are saved in a “Species Þle” (extension*.cxp), which is a text Þle declaring the parametersand deÞning their respective values. Species Þles areoften created using an initial set of parameters storedin a “Species template” of an organism with a similardistribution.

Annual growth index is calculated as the arithmeticmean of the weekly GI values � 100.0. Stress and stressinteraction indices are combined into annual indicesby multiplying individual component stress factorsafter scaling. Subtractionof theannual stress andstressinteraction indices from the annual growth index re-sults in the ecoclimatic index (EI), which is scaledfrom 0 to 100. EI is an overall measure of the favor-ableness of the location for permanent occupation onthe species of interest. EI � 0 indicates poor prospectsfor long-term survival; 100 reßects the constant favor-able conditions found in incubators. EI may be re-duced to 0 because of two constraints: (1) insufÞcientthermal accumulation (degree-days) to complete de-velopment and (2) obligate diapause requirementscannot be met. Simulation results can be presented astables, graphs, or maps and saved in appropriate for-mats for use in other software applications.Model Parameterization. A species Þle for C. cac-

torum was created from the temperate species tem-plate available in CLIMEX (ver. 2). Temperature in-dex parameters were estimated using laboratory dataon development time at different constant tempera-tures(McLeanet al. 2006,Legaspi andLegaspi 2007b).Development rate for egg to pupal stages was esti-mated by the logisitic equation: rate � 0.0165/[1 �(T/20.7093)�5.8823] (SE � 0.0020, 1.2651, and 2.1466,respectively; F� 24.93; df � 2,4; P� 0.05; R2 � 0.92).Regression of the linear portion of the curve resultedin an estimated lower developmental threshold tem-perature of 13.3�C (rate � �0.0133 � 0.0010T; SE �0.0034 and 0.0002, respectively; F � 45.49, P � 0.09;R2 � 0.96). The degree-day requirement for develop-ment was calculated asDD� (T Ð T0)D,whereDD isdegree-days, T is temperature tested, T0 is thresholdtemperature, and D is duration time at that temper-ature, resulting in estimated degree-days for develop-ment from �845 at 18�C to 1,387 at 34�C (Legaspi andLegaspi 2007b).

The moisture index parameters were estimated us-ing Vera et al. (2002), who modeled the Mediterra-

nean fruitßy,Ceratitis capitata (Diptera: Tephritidae),using thenativeSouthAmericandistribution similar tothat ofC. cactorum.The target area of distribution wasthe documented native habitat of Paraguay, Uruguay,southern Brazil, and northern Argentina (Zimmer-mann et al. 2004). The model was run iteratively withand without each stress component (cold, heat, dry,and wet), stress interactions (cold-dry, cold-wet, hot-dry, and hot-wet), and light and diapause indexes todetermine which factors inßuenced distribution inSouth America. Final parameter estimates are shownin Table 1.Global Distribution Patterns. Following CLIMEX

procedures, when the model distribution approxi-mated that of the target distribution, other areas wereexamined for potential geographic extension. The pa-rameter Þle for C. cactorum was run using the “Com-pare Locations” function to examine predicted distri-butions throughout the world. The model used aglobal climate surface consisting of climatic averagescalculated into a 0.5� grid and maintained by the In-tergovernmental Panel on Climate Change (IPCC;http://ipcc-ddc.cru.uea.ac.uk/; IPCC-TGCIA 1999).Potential global insect distribution was examined.Field-Level Validation. Cactoblastis cactorum eggs

sticks were obtained from a laboratory colony rearedat USDAÐARSÐCMAVE/FAMUÐCBC in Tallahassee,FL, and placed on cactus pads inside Þeld cages for anentire calendar year at the St. Marks National WildlifeRefuge, St. Marks, FL (30.160 �N, 84.206 �W) (Legaspiet al. 2009a). Three trials were performed to corre-spond to the three generations ofC. cactorum through-out the year (Legaspi et al. 2009a). The duration of thethree different trials were as follows: trial 1 (October2006ÐMay 2007); trial 2 (MayÐAugust 2007); and trial3 (AugustÐOctober 2007). Potted cactus plants wereprepared by placing cactus plant cuttings (Opuntiaficus-indica) in plastic pots (28 cm diameter by 29 cmheight; Nursery Supplies, Chambersburg, PA) �30 dbefore the start of each trial. A minimum of 66 cactusmoth egg sticks of the same age were collected andplaced in a growth chamber (Thermoforma, Marietta,OH) at 26�C with a photoperiod of 14:10 (L:D) and50 10% RH. Egg sticks were placed individually into30-ml plastic cups (Solo, Highland Park, IL) and cov-ered with a cardboard lid. Approximately 5Ð7 d beforethe eggs hatched, the number of eggs per egg stick wasrecorded. A piece of cactus (20 mm length by 20 mmwidth) was placed in each cup as soon as the eggshatched. One to 2 d after the eggs hatched, the num-bers of eggs that hatched were recorded and the cac-tus pieces containing the Þrst-instar larvae were takento St. Marks National Wildlife Refuge. In an area nearthe picnic pond at St. Marks, 22 screen cages (60 by 60cm; Bioquip, Rancho Dominguez, CA) were placed�60 cm apart. Three potted cactus plants were placedinside each screen cage. One cactus piece with Þrst-instar larvae was pinned using an entomological pin(#2; Bioquip) onto the upper cactus pad in each pot. AHOBO weather recorder (Onset Computer, Bourne,MA) was placed outside the cages to record weatherdata. After 1Ð2 wk, one cage with three potted plants

370 ENVIRONMENTAL ENTOMOLOGY Vol. 39, no. 2

was taken to the laboratory for sampling. Thereafter,one cage per week was sampled until all adultsemerged or all cages were returned. When necessarybecause of increased larval development rate, cageswere examined twice weekly. In the laboratory, eachcactus pad was dissected to determine the number andstage of cactus moth larvae. The larval stages were

determined through the body length and head capsulewidth measurements (mm) of a minimum of 5 larvaeper plant for a minimum of 15 larvae per cage. Bodylength was measured from the dorsum of the head tothe posterior end of the abdomen.

Differences between weekly measurements were re-corded and calculated as daily rates of change. These

Fig. 1. World map of potential distribution. CLIMEX calculations of EI (see text for description) are overlaid onto a mapof known moth distribution. Higher EI values and darker shading indicate conditions favorable for the cactus moth. Knowndistributions are shown in the circles. The native distribution is indicated.

Table 1. CLIMEX parameter file for Cactoblastis

Parameter Description (see Sutherst et al. 2004) Value

Temperature index (Legaspi and Legaspi 2007a)DV0 Lower temperature threshold 9DV1 Lower optimum temperature 25DV2 Upper optimum temperature 30DV3 Upper temperature threshold 36

Moisture index (Vera et al. 2002)SM0 Lower soil moisture threshold 0.1SM1 Lower optimal soil moisture 0.2SM2 Upper optimal soil moisture 0.8SM3 Upper soil moisture threshold 1.0

Cold stressTTCS Cold stress temperature threshold 9.0THCS Cold stress temperature rate 0DTCS Cold stress degree-day threshold 0DHCS Cold stress degree-day rate �0.0001TTCSA Cold stress temperature threshold (average) 9.0THCSA Cold stress temperature rate (average) �1.0

Heat stressSMDS Dry stress threshold 0.01HDS Dry stress rate �0.1

Wet stressSMWS Wet stress threshold 1.2HWS Wet stress rate 0.0015PDD Degree-days per generation (Legaspi and Legaspi 2007a) 1,500

Reproduced with permission from Capinera 2008.


differences in growth measurements were plottedagainst the predicted CLIMEX growth index for thecorresponding day of the year using historical weatherdata and grid coordinates closest to the actual Þeld site(30.3� N, 84.3� W). Afterward, a weather Þle speciÞc tothe time and location of the sampling site was createdusing temperature data recorded using a HOBO datalogger situated in the experimental site. Rainfall andhumidity data during the time of the study was collectedfrom the Florida Automated Weather Network (Uni-versity of Florida, http://fawn.ifas.uß.edu) for the near-estweatherstation(Carrabelle,FL;29.73�N,85.027�W).Predictedgrowthindiceswerecomparedwithmeasuredlarval growth rates over the year.

Results and Discussion

GlobalDistribution Patterns.Predicted and knownworldwide distributions of C. cactorum are shown to-

gether in Fig. 1. In its native geographic range, coldstress may be limiting distribution to the south andalong the Andes mountain range, whereas wet stressseems to prevent distribution in northern Brazil. Sim-ilar results were obtained by Soberon et al. (2001).Lack of records of C. cactorum may be caused by theabsence of host cactus species or simply lack of effortto collect the moth in certain areas. CLIMEX pre-dicted limited potential distribution in North America(from the Caribbean Islands to Florida, Texas, andMexico), Africa (South Africa, and parts of the easterncoast), southern India, parts of Southeast Asia, and inthe northeastern coast of Australia. Actual recordsindicate the moth has been found in the Caribbean(Antigua, Barbuda, Montserrat Saint Kitts and Nevis,Cayman Islands, and U.S. Virgin Islands), Cuba, Ba-hamas, Puerto Rico southern Africa, Kenya, Mexico,and Australia (Zimmermann et al. 2000). However,CLIMEX did not predict that distribution would ex-

Fig. 2. (A) CLIMEX growth, temperature, and moisture indices and (B) weather data using historical global weather datafor grid location closest to St. Marks, FL (30.3� N, 84.3� W).


tend from India to the west into Pakistan where it wasintroduced. Based on current distribution limits ofSouth Carolina on the Atlantic Coast and Louisianaalong the Gulf Coast, the moth may be close to itspredicted northern range along the Atlantic coast.Parts of Texas and most of Mexico appear vulnerable.Field-Level Validation.CLIMEX output using his-

torical weather data are shown in Fig. 2. The data areadjusted to be shown from November to Novemberto coincide with the Þeld sampling schedule.CLIMEX predicts only one period of favorablegrowth, with growth potential apparently limited bythe availability of moisture (MI). The regression ofbody rate against head rate was signiÞcant, indicat-ing that rate of change of head width capsule is agood predictor of rate of body length (body rate�0.173 � 10.042 � head rate; F� 30.9; df � 1,28;R2 �0.51; P� 0.01). Predicted growth indices are super-imposed over measured larval growth rates and did

not compare favorably with daily larval growth asmeasured in head capsule width or body length(Fig. 3). Measurements were not taken using thesame insects, but rather cohorts laid at the sametime. Therefore, experimental error sometimesresulted in measurements that were smaller thanthose taken the week before, thus resulting in neg-ative estimated growth rates. Larval growth ratesdid not account for egg, pupal, or adult develop-ment. The arrows at the top of the Þgure indicateestimate generation time for each of the three C.cactorum generations in St. Marks, FL (Legaspi et al.2009a). The actual weather data collected at the siteor at the Carrabelle weather station during the timeof the study are shown in Fig. 4. The temperatureproÞles are similar to those of the CLIMEX histor-ical weather data (Fig. 2), but recorded rainfall wasmuch higher, leading to higher calculated grownindices (Fig. 5). The higher rainfall resulted in

Fig. 3. Growth index (using historical weather data) for St. Marks, FL, plotted against (A) rate of head capsule change(mm/d) and (B) rate of body length change (mm/d). Arrows above each graph indicate approximate generation times ofC. cactorum in the Þeld.


higher growth index values throughout the year andseems to be in closer agreement with measuredlarval growth rates. The GI curves correspond ap-proximately to the larval growth of the three gen-erations found in the Þeld. Within each generation,continued favorable GI levels should correspond todevelopment of pupal and adult insects, althoughthese were not measured here.

Relatively few papers have addressed the need tovalidate CLIMEX predictions, and more validationefforts are needed (Sutherst and Maywald 2005,Sutherst and Bourne 2008). Most validation effortsfocus on testing large-scale CLIMEX predictions onspecies geographical distribution. Norval and Perry(1990) developed a CLIMEX model of the brownear-tick, Rhipicephalus appendiculatus Neumann(Ixodida: Amblyommidae) in Zimbabwe. They con-cluded that the occurrence of the tick because ofwet conditions, as well as its disappearance becauseof heat stress, served as validations of their model.Venette and Cohen (2006) developed a CLIMEXmodel for the oak pathogen Phytophthora ramorum(S. Werres, A.W.A.M. de Cock and W.A.Man inÕtVeld) using a specialized weather data set and pa-rameter estimates from the literature. Lack of dataon worldwide distribution patterns precluded theuse of the iterative geographic Þtting process.Model predictions matched known occurrences ofP. ramorum in California and Oregon. The pathogenwas 3.4 times more likely to occur in areas classiÞedas favorable or highly favorable than in those clas-siÞed as marginal or unsuitable. Another recent ex-ample of CLIMEX validation was the use ofCLIMEX to predict the naturalization potential ofgenetically modiÞed and nontransgenic upland cot-ton (Gossypium hirsutum variety hirsutum) in Aus-tralia (Rogers et al. 2007). Climate-based predic-tions of potential distribution indicated distribution

potential only in the coastal regions of northeastAustralia. Predictions were reÞned by overlayingsoil nutrient and existing land use data, resulting infurther restriction mostly to the wet tropics incoastal northeast Australia. A subsequent 3-yr sur-vey appeared to validate model prediction (Addisonet al. 2007). A CLIMEX model for the fungal plantpathogen Pyrenophora semeniperda (Brittlebankand Adam) Shoemaker was developed to estimateits potential global distribution based on climaticsuitability (Yonow et al. 2004). The model correctlypredicted all known locations for the pathogen, withonly known Þve locations classiÞed as unsuitable.The authors concluded the CLIMEX model to be anaccurate predictor of potential geographic distribu-tion and the Þve “outlier” locations were likely to betransient populations (Yonow et al. 2004).

Sutherst and Bourne (2008) compared potentialdistribution of an invasive species, the bovine tick,Rhipicephalus (Boophilus) micrplus (Canestrini)(Acari: Ixodidae) in Africa as predicted by a mul-tivariate logistic regression model and a CLIMEXmodel. The regression model correctly predictedthe spatial data, but not the range extensions. TheCLIMEX model included the observed distribution,but also large areas outside the range of observa-tions, which included the range extensions. Theselection of an invasive species was important inthat the study focused on a system that had notreached equilibrium. The authors conclude that sta-tistical models are best for interpolation, not ex-trapolation because areas not yet colonized are cat-egorized as unsuitable. Given these limitations,Sutherst and Bourne (2008) question the validity ofpredicting changes to species ranges caused bytranslocation or climate change.

We encountered the typical problem of an in-complete presence and absence species data set.

Fig. 4. Weather data created for St. Marks, FL. Temperature maxima and minima are from data loggers on-site. Rainfallwas from Carrabelle, FL (November 2006 to November 2007).


The problems in collecting data to both calibrateand validate the CLIMEX global distribution mapsare likely to be typical for most scientists attemptingsimilar studies and are similar to those experiencedin earlier attempts to model the spined soldier bug,Podisus maculiventis (Say) (Heteroptera: Pentato-midae) (Legaspi and Legaspi 2007a). Detailed dis-tribution records were difÞcult to obtain, despitethe fact that C. cactorum is a well-documented in-sect. It is difÞcult to determine deÞnitively whetherthe absence of records from large regions such aseastern Brazil is because of failure to collect themoth when sampling were conducted or simply lackof sampling efforts. Absence data are almost as im-portant as presence data (Soberon and Peterson2005). In addition to problems with distributionrecord data, weather data may not be available forspeciÞc times and locations of interest. Statistical

techniques to determine whether predicted distri-butions matched actual distributions are problem-atic (but see Gevrey and Worner 2006, Heikkinen etal. 2006). Another potentially signiÞcant complica-tion is that strains of different geographical originshave differing bionomics perhaps as adaptations tolocal climate.

Although most CLIMEX validation studies havefocused on predicted spatial distributions, few haveattempted validation of temporal predictions onsmaller scales. In small-scale temporal validationstudies, the selection of an appropriate metric iscritical. Legaspi and Legaspi (2007a) and Rafoss andS¾thre (2003) both used pheromone trap counts tovalidate CLIMEX models for P. maculiventris andthe codling moth, Cydia pomonella L. (Lepidoptera:Tortricidae), respectively. A limitation to this ap-proach is that immature stages were not recorded.

Fig. 5. Growth index (site-speciÞc weather data) for St. Marks, FL, plotted against (A) rate of head capsule change(mm/d) and (B) rate of body length change (mm/d). Arrows above each graph indicate approximate generation times ofC. cactorum in the Þeld.


The growth index estimates the degree to whichclimatic conditions are favorable for insect devel-opment and may be poorly correlated to adultcounts. Legaspi and Legaspi (2007a) reported aweak correlation between pooled pheromone adulttrap counts and corresponding growth indices, sug-gesting that stronger correlations may be found us-ing location-speciÞc weather data and accuratemeasurements of larval growth. Rafoss and S¾thre(2003) found no relationship between codling mothtrap counts in Norway and CLIMEX ecoclimatic andgrowth indices. They suggested that pheromonetraps may not provide accurate measurements ofpopulation densities and that insect counts are af-fected by several factors, including temperature,moonlight, wind speed, and trap or lure placement.

In conclusion, bioclimatic models in general, andCLIMEX models in particular, require more valida-tion (Sutherst and Maywald 2005). Invasive pests,such as C. cactorum, have not reached equilibriumdistributions and present unique opportunities tovalidate models by comparing predicted distribu-tions with eventual realized geographic ranges(Sutherst and Bourne 2008). Only time and morecomplete records will tell whether C. cactorum willextend its geographical distribution to regions pre-dicted by the CLIMEX model. Potential range ex-tension into Mexico must be carefully monitoredbecause of the value of the cactus industry. In termsof small-scale temporal predictions, the data pre-sented here suggest that CLIMEX indices may agreewith Þeld-speciÞc population dynamics, providedan adequate metric for insect growth rate is used,and weather data are location and time speciÞc.

Acknowledgments

We thank S. Hight (USDAÐARSÐCMAVE/FAMUÐCBC,Tallahassee, FL) for providing some of the weather data andfor helpful discussions. J. Carpenter (USDAÐARS, Tifton,GA) and his staff provided useful information regarding massrearing of the cactus moth. D. Borchert (USDAÐAPHISÐPPQÐCPHST, Raleigh, NC), M. Pescador (Florida A&M Uni-versity), J. Carpenter, H. M. Sandler (Vanderbilt University,Nashville, TN), and anonymous reviewers provided helpfulcomments on the manuscript. I. Baez, E. Aninakwa, M. Ed-wards, K. Marshall, Jr., S. Baez, J. Mass, C. Albanese (USDAÐARSÐCMAVE/FAMUÐCBC, Tallahassee, FL), S. Henderson,and W. Zeigler (Florida A&M University, Tallahassee, FL)assisted in Þeld sampling. We are grateful to J. Reinman (Wild-life Biologist, St. Marks National Wildlife Refuge, St. Marks, FL)for facilitating the use of the Þeld sites at the refuge.

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Received 30 September 2008; accepted 26 October 2009.


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