Agricultural and Forest Entomology (2010), 12, 417–426 DOI: 10.1111/j.1461-9563.2010.00488.x

Spatial analysis of gypsy moth populations in Sardinia usinggeostatistical and climate models

Arturo Cocco, Antonio Q. Cossu∗, Patrizia Erre†, Giovanni Nieddu† and Pietro Luciano

Department of Plant Protection, University of Sassari, via De Nicola 1, 07100 Sassari, Italy, ∗Sardinian Environmental Protection Agency, via

Rockefeller 58-60, 07100 Sassari, Italy and †Department of Economics and Woody Plant Ecosystems, University of Sassari, via De Nicola 1, 07100Sassari, Italy

Abstract 1 Spatial fluctuations of the Sardinian population of the gypsy moth Lymantria dispar(L.) (Lepidoptera: Lymantriidae) were characterized using geostatistical and climatemodels. Data on gypsy moth egg mass abundance recorded at 282 permanentmonitoring sites from 1980 to 2004 were incorporated in a geographic informationsystem with the vegetational, geomorphological and pedological features of the sites.

2 Statistical analyses revealed that the relative outbreak frequency was related to thepredominant host tree, slope and elevation of the monitoring sites, whereas therewas no correlation between outbreak frequency and exposure and soil type.

3 By using bioclimatic modelling, probability maps of gypsy moth outbreaks weregenerated. The model identified a probability surface with climatic conditionsfavourable to gypsy moth outbreaks and thus potentially subject to defoliation.The maps included 92 sites where outbreaks never occurred, suggesting that theSardinian climate may not be a determinant factor for gypsy moth outbreaks.

4 The geostatistical method cokriging with outbreak frequency as a covariate wasfound to be the most suitable technique to estimate gypsy moth egg mass abundance.Semivariograms showed spatial correlation of egg mass abundance within therange 18.5–53 km. The results obtained were used to create regional gypsy mothdistribution maps by cokriging, which demonstrated the outbreak foci and differentinfestation levels at each monitoring area. These results can help to delimit thetreatment areas and develop rational gypsy moth management programmes.

Keywords FloraMap, geographic information system, kriging, Lymantria dispar,probability map, spatial distribution.

Introduction

The protection of forests has assumed increasing internationalimportance in recent years because of the undisputed economicand environmental importance of those ecosystems. Oakscharacterize the natural landscape in the Mediterranean area,with pure or mixed stands of holm oak Quercus ilex L.,pubescent oak Quercus pubescens Willd. and cork oak Quercussuber L. On the island of Sardinia, oak forests cover 18%of the regional surface (4400 km2) (Regione Autonoma dellaSardegna, 2003). There is a gradual worsening of the health ofthese ecosystems, however, as a result of the spread of ‘oakdecline’, caused by a combination of abiotic and biotic factors

Correspondence: Arturo Cocco. Tel: +39 079229245; fax: +39079229329; e-mail: acocco@uniss.it

(Franceschini et al., 1999). One of the dominant biotic factorsis defoliation by lepidopterous pests.

The gypsy moth Lymantria dispar (L.) (Lepidoptera:Lymantriidae) is one of the main forest defoliators, both withrespect to the intensity of its outbreaks and frequency of attacks;it is mainly present in European, southern Asian and NorthAmerican oak forests. When gypsy moth attacks coincide withabiotic (e.g. drought) or biotic stresses (secondary insect orfungal attacks), they can seriously compromise the health offorest stands. Studies conducted in North America, Europe andAsia showed the tendency of the gypsy moth population tooscillate at periodicity in the range 5–10 years (Di Cola et al.,1995; Johnson et al., 2005). In different forested areas of Sar-dinia, gypsy moth exhibited three distinct outbreak intervals(i.e. 5–6 years, 8–9 years and over 10 years), depending onforest type and management strategy (mixed or pure stands,

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418 A. Cocco et al.

complexity of the undergrowth vegetation, wooded pasture)(Luciano et al., 1999, 2002).

The complex dynamics of gypsy moth populations makesit difficult to predict fluctuations in gypsy moth abundance(Liebhold et al., 2000). The spatial distribution of gypsy mothdefoliations has been analyzed by geostatistical methods topredict defoliated areas (Hohn et al., 1993; Liebhold et al.,1998b), and statistical and climatic models have been developedin recent years to study the global distribution of gypsymoth (Gevrey & Worner, 2006) and spatially synchronouspopulation dynamics in North America (Liebhold et al., 2006).Climatic envelope models (Jones & Gladkov, 1999) have beensuccessfully employed to identify areas particularly susceptibleto lepidopterous attacks in Central America using data basedonly on the presence/absence of the pest (Soberon et al., 2001;Peterson et al., 2002).

The present study aimed: (i) to analyse the influence ofvegetational, geomorphological and pedological characteristicson the spatial distribution of gypsy moth outbreaks in Sardinia;(ii) to investigate how climatic conditions affect outbreakoccurrence by using a climatic model; and (iii) to creategypsy moth distribution maps based on the abundance of eggmasses recorded in a network of monitoring sites. The mapscan provide useful information for the rational management ofresources used for the monitoring and control of gypsy mothoutbreaks.

Materials and methods

Study area

Gypsy moth population dynamics were recorded in the period1980–2004 in the main cork, holm and pubescent oak areasof Sardinia based on 282 monitoring sites distributed overan area of approximately 2200 km2 (Fig. 1) (Luciano, 1989).From 1980 to 1983, the egg mass abundance was recordedin 107, 108, 109 and 166 sites, respectively. From 1984 to2004, a mean ± SD of 280 ± 2 sites were monitored (range276–282). The monitoring sites were divided into 12 districtsbased on the slope and elevation of the territory, the landuse, the predominant host tree species, and the surface areaof continuous forestation.

Data collection

The population abundance of the gypsy moth was monitoredby counting the egg masses on 10 contiguous oak treesalong the four cardinal directions starting from a commoncentral reference point, for a total of 40 trees at each site(Fraval et al., 1978). Counts of egg masses are more suitablethan pupae or male moth counts to predict gypsy mothdefoliation on a regional scale (Liebhold et al., 1995a). Thesites were georeferenced and classified according to theirgeomorphological, vegetational and pedological features. Adigital elevation model with a resolution of 20 m was producedusing the Regional Technical Map of Sardinia (1 : 10 000)and was used to identify the characteristics of exposure,slope and elevation of the monitoring sites. The predominant

Figure 1 Location of the gypsy moth monitoring sites and forest districtsin Sardinia.

host tree species was determined by visual inspection oftrees at each monitoring site (Luciano, 1989), whereas thepedological features of the sites were taken from the Soil Mapof Sardinia (1 : 250 000) (Madrau et al., 2006). The data setswere combined in a geographic information system (GIS) usingarcgis, version 9.0 (ESRI, 2004).

Statistical analysis

Gypsy moth populations at each site were evaluated by definingan outbreak threshold of 100 egg masses per site, whichcauses the complete defoliation of the sampling area (Lucianoet al., 2002). The relative frequency of outbreaks (intended asthe percentage of years in which an outbreak was recordedduring the study years) was chosen as the dependent variableand was correlated with the vegetational, pedological andgeomorphological features of the monitoring sites by Spearman

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Agricultural and Forest Entomology © 2010 The Royal Entomological Society, Agricultural and Forest Entomology, 12, 417–426

Gypsy moth spatial fluctuation in Sardinia 419

rank correlation, comprising a nonparametric measure of thecorrelation between two variables. Spearman coefficients wereemployed to evaluate the influence of geomorphological andclimatic factors on the distribution of plant and insect species(De Liberato et al., 2003; Erre et al., 2009). The nonparametrictest was employed to avoid assumptions on normal distributionof the variables and to analyze categorical (exposure, soil typeand tree species) and numeric (slope and elevation) parameterswith the same procedure.

The preference of the gypsy moth for a specific host treewas studied by comparing the relative frequency of outbreaksrecorded and the predominant tree species at each monitoringsite. The data were evaluated with analysis of variance usingproc glimmix (SAS Institute, 2002) and the differences amonggroups were revealed by the Tukey–Kramer test.

The spatial distribution of the gypsy moth was related tothe climatic conditions in Sardinia using the bioclimatic modelFloraMap, which assumes that the climate at the monitoringsites is a favourable environmental factor that affects thedevelopment of an organism (Jones & Gladkov, 1999; Morales& Jones, 2004). Latitude, longitude and elevation of eachmonitoring site were used as a calibration set to implementthe model. Using thin-plate smoothing, 36 climatic layers(12 monthly means for temperature, rainfall and diurnaltemperature range) with an approximate resolution of 10 km(Hijmans et al., 2005) were calculated. Temperature and rainfalldata were obtained by interpolating the climatic data recorded at240 meteorological stations of the Governmental HydrographicService from 1961 to 2005 (Cossu et al., 2005). Fouriertransform and principal components analysis were employedto standardize the climatic seasonality for the calculation ofdifferent multidimensional probability distributions. The 172monitoring sites where outbreaks occurred were classified assites with a low, medium or high frequency of gypsy mothoutbreaks (relative frequencies of <10, 10–40 and >40%,respectively), thereby creating three outbreak probability maps.On the basis of climatic data obtained from monitoring siteswhere outbreaks occurred, outbreak probability maps definesareas with climatic features potentially favourable to outbreakscalculated with 20% as the minimum probability of gypsy mothpresence. In addition, the geographical position of the 110 siteswhere no outbreaks had ever been recorded was overlaid on theoutbreak probability areas aiming to further investigate whetherthe absence of outbreaks was a result of climatic features.

The distribution and spatial correlation of the annual abun-dance of gypsy moth egg masses in Sardinia were analyzed withgeostatistical methods. The annual egg mass data had a skeweddistribution and the frequency histogram showed two prevalentclasses: zero (not infested) and over 1400 egg masses per site.Therefore, the values were approximated to a normal distribu-tion by logarithmic transformation log (n + 1) before analysis(Liebhold et al., 1991). Omnidirectional semivariograms weremodelled using gs + 7.0 with a resolution of 250 m (GammaDesign Software, 2004) using 15 equal-sized distance classesand a maximum distance of 98.3 km (approximately half themaximum distance between data pairs). Models tested by thesoftware were Gaussian, exponential and spherical (Ifoulis &Savopoulou-Soultani, 2006; Sciarretta & Trematerra, 2006). Foreach model best fitting the data, the parameters nugget (C0),

range (A), sill (C0 + C, where C is the variability attributable tothe spatial dependence of the data), and C/(C0 + C) were cal-culated (Ellsbury et al., 1998). The C/(C0 + C) ratio representsthe percentage of variability attributable to spatial dependence,where values higher than 0.75 indicate a strong spatial relation-ship between the data (Ifoulis & Savopoulou-Soultani, 2006).Models obtained from variogram analyses were used to estimatethe gypsy moth egg mass abundance by kriging.

Because the egg mass abundance is influenced by numerousfactors, the analyses were also carried out with the cokrigingmethod, associating covariates with the principal variable eggmass abundance (Liebhold et al., 1993). The elevation andslope of the sites were chosen as covariates on the basis of theSpearman correlation coefficients (see Results). The relativefrequency of outbreaks was used because the frequency ofdefoliation has been previously used as a measure of forestsusceptibility to predict gypsy moth defoliations (Gribko et al.,1995). Omnidirectional cross-semivariograms were computedapplying the same settings used to calculate semivariograms.Kriging and cokriging estimations were statistically validatedwith the cross-validation method using gs + 7.0 (Geisser,1975). The interpolations were imported into arcgis 9.0 (ESRI,2004) to create spatial distribution maps of the gypsy moth eggmasses.

Results

Gypsy moth temporal patterns

The relative frequency of gypsy moth outbreaks during thestudy period generally showed peaks every 7–8 years (Fig. 2).The fluctuations were particularly evident until the beginningof the 1990s, with peaks in 1982 and 1990. In the subsequentperiod, the trend was discontinuous, although a peak with amuch lower percentage of infested sites can be recognized in1997. From 2001, treatments based on Bacillus thuringiensisBerliner subsp. kurstaki were carried out in the specificsites where outbreak started (foci) to limit infestations ofthe gypsy moth and the lackey moth Malacosoma neustrium(L.) (Lepidoptera: Lasiocampidae). Only 1.4%, 1.5%, 2.7% and2.7% of the surface of Sardinian oak forests, however, wastreated in 2001, 2002, 2003 and 2004, respectively (Lucianoet al., 2003).

0

20

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1986

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2004R

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Figure 2 Temporal pattern of the relative frequency of gypsy mothoutbreaks in Sardinia from 1980–2004.

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420 A. Cocco et al.

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ativ

e fr

eque

ncy

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utbr

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)District 1District 2

Figure 3 Temporal patterns of the relative frequency of gypsy mothoutbreaks in districts 1 and 2 from 1980–2004.

Oscillations in gypsy moth populations were analyzed ineach district, showing different patterns. Exemplary gypsymoth trends in two homogeneous districts (1 and 2) arepresented in Fig. 3. These districts are characterized by amarked homogeneity and extension of tree coverage and byintensive economic exploitation of the cork oak forests. Indistrict 1, the outbreak peaks occurred every 7–8 years, in1982, 1990 and 1997. However, in district 2, only the first twopeaks were contemporaneous with those of district 1 becausethe oscillation was altered by peaks at 5–6-year intervals (1996)and high infestation levels over several years (1999–2000).

Influence of soil and geomorphological features on gypsymoth outbreaks

There were significant negative correlations between outbreakoccurrence and slope (ρ = −0.43; d.f. = 280; P < 0.0001)and elevation (ρ = −0.39; d.f. = 280; P < 0.0001), whereassignificant positive correlation was found between outbreakoccurrence and predominant tree species (ρ = 0.29; d.f. =280; P < 0.0001) (Table 1). Correlations between the outbreakoccurrence and exposure (ρ = −0.06; d.f. = 280; P = 0.0800)and soil type (ρ = −0.10; d.f. = 280; P = 0.0684) were notsignificant.

Gypsy moth outbreaks occurred more frequently in pure corkoak woods (F = 40.73; d.f. = 6, 275; P < 0.0001) (Table 2).Mixed stands of cork oak associated with pubescent oak orassociated with holm oak and pubescent oak and pure woodsof pubescent oak exhibited relative frequencies of outbreaks of

Table 1 Spearman correlations between the relative frequency of gypsymoth outbreaks and soil, geomorphological and vegetational features ofthe monitoring sites (n = 282)

Independent variable Spearman correlation (ρ) P-value

Slope –0.43∗ <0.0001Elevation –0.39∗ <0.0001Exposure –0.06NS 0.0800Soil type –0.10NS 0.0684Predominant tree species 0.29∗ <0.0001

∗P < 0.01.NS, not significant.

Table 2 Mean relative frequency of gypsy moth outbreaks in monitoringsites (n = 282) with different predominant host trees (1980–2004)

Predominant tree speciesMean ± SE relativefrequency of outbreaks (%)

Cork oak 21 ± 1.4a

Cork oak + holm oak 1 ± 0.5c

Cork oak + pubescent oak 9 ± 4.8b

Cork oak + holm oak + pubescent oak 9 ± 4.3b

Holm oak 1 ± 0.4c

Holm oak + pubescent oak 2 ± 2.2bc

Pubescent oak 6 ± 1.9b

Significant differences compared with PROC GLIMMIX (P < 0.0001),means with the same superscript letter are not significantly different(Tukey–Kramer test, P < 0.05).

9%, 9% and 6% respectively, which were significantly differentfrom those of mixed stands of cork oak and holm oak and purewoods of holm oak (relative frequencies of outbreaks of 1%for both).

Climatic model

The relationship between gypsy moth spatial distribution andthe climatic characteristics of the Sardinian wooded areaswas further analyzed with the bioclimatic model (Fig. 4).Respectively, 94%, 84% and 94% of the sites with high,medium or low frequency of outbreaks were within therespective probability areas, emphasizing the reliability of themodel. Monitoring sites with low-outbreak frequency werepresent in all forest districts except 3 and 10, whereas theobservation points characterized by medium or high frequencyof outbreaks were concentrated in districts 1, 2 and 5 andin districts 3 and 6, respectively. The monitoring sites withhighest outbreak probability (>80%) were those with mediumfrequency of outbreaks in districts 1 and 5 (69% and 50% of thetotal district sites, respectively), predominantly on pure standsof cork oak (Fig. 4).

Overlaying the three prediction maps, a broad probabilitysurface with climatic conditions that allow the developmentof harmful populations of the gypsy moth was generated(Fig. 5). This area thoroughly delimited the potential gypsymoth distribution because it included 89% of the Sardinianoak forests according to the Corine classification (1 : 25 000)(Regione Autonoma della Sardegna, 2003). Of the 172monitoring sites where outbreaks occurred, 88% (n = 152) fellwithin the overlay of the probability maps. Surprisingly, 84%(92 of 110) of the sites where outbreaks never occurred werewithin the potential distribution surfaces defined by the model.

Gypsy moth spatial distribution

Determination coefficients of semivariogram and cross-semi-variogram models, used respectively for kriging and cokriginginterpolations, were always significant, except for 1980 (krigingand cokriging with covariates elevation and slope), 1981 and1982 (kriging), and 1983 (kriging and cokriging with covariateslope) (Table 3). Cokriging, with covariate relative frequency

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Gypsy moth spatial fluctuation in Sardinia 421

Figure 4 Probability maps of low (A), medium (B) or high (C) frequency of gypsy moth outbreaks in Sardinia. White squares represent monitoring siteswith low (A), medium (B) and high (C) outbreak frequency, respectively.

of outbreaks, always showed significant r2 values, which werehigher than those obtained with the other estimation proceduresin 12 of the 25 data sets (1980–1983, 1985, 1988–1990, 1993,1996, 1998 and 2002).

The cross-semivariograms showed that no single modelprovides a best fit of the data because of the high interannualvariability of egg mass abundance (Table 4). The sphericalmodel best represented the data in 14 of the 25 data sets. Therange showed spatial dependence between 18.5 and 53.1 km,with a mean ± SE of 35.3 ± 1.9 km. Table 4 shows thatnugget values are always close to zero and the ratio C/(C0 + C)is always greater than 0.75.

The estimations were significant (P < 0.01) in all studyyears, except in 1982, 1983 and 2004 (Table 5). In 1980 and1981, the low value of the cross-validation coefficients (0.30and 0.28, respectively) could be attributed to the small numberof sites monitored (107 and 108, respectively). Generally, thecoefficients were lower when outbreaks occurred at less than10% of the sites (1985, 1986, 1993, 1995, 2003 and 2004). In1984, 1987 and 2002, the geostatistical analyses showed highcoefficient values (0.84, 0.58 and 0.58, respectively) despite thefact that the outbreaks were recorded respectively in 5%, 7%and 9% of the monitoring sites. These results can be explainedconsidering that the few infested sites were very close, givinga high reliability to the estimation.

Spatial analyses were used to generate distribution mapsshowing the spatio–temporal evolution of the outbreaks, thefoci in the various forest districts and the areas most frequentlydefoliated by the gypsy moth. The more relevant contourmaps are shown in Fig. 6. They reveal the spread of thegypsy moth infestation in different forest districts from 1986to 1989 (progradation phase), the maximum extent of theinfestation (culmination) in 1990 (41% of the sites) and therapid decrease of the population abundance from 1991 to 1993

(retrogradation). In 1986 and 1987 the outbreak foci werelocated in some small central–northern areas of Sardinia. Inthe subsequent years, the infestation spread to the surroundingareas, with the highest extent in 1990. From 1991 to 1993, theextent of infestation decreased in the central–northern districts,whereas some foci appeared in the southern districts 11 (1992)and 12 (1993).

Discussion

The gypsy moth is a polyphagous pest, attacking over500 species worldwide (Liebhold et al., 1995b). Tree suitabilityto gypsy moth and its performance on host species have beenreported from North America (Liebhold et al., 1995b; Foss &Rieske, 2003). Little information is available, however, on thepreference of L. dispar to the most common oak species inthe Mediterranean Basin (cork oak, pubescent oak and holmoak). The results obtained in the present study show that theoutbreak occurrence is related to the predominant host species,in accordance with laboratory experiments where gypsy mothlarvae raised on cork oak foliage showed a higher growth rate,greater pupal weight and higher adult fertility than those fedon holm or pubescent oak (Luciano et al., 1999).

The relationships between the geomorphological variablesslope and elevation and the relative frequency of outbreakscould be a result of the different complexity of undergrowthvegetation on oak stands. Forests at high elevation and onvery steep slopes are not intensively exploited for sheephusbandry and generally well preserved, because they are notsubject to pasture improvement or forage cultivation (Luciano& Prota, 1995). Oak stands at elevations below 600 m abovesea level on moderate slopes are subject to intense grazing,often with excessive livestock loads, thus degraded to pastureswith scattered trees. Gypsy moth parasitoids and generalist

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422 A. Cocco et al.

Figure 5 Overlay of outbreak probability maps and location of themonitoring sites where gypsy moth outbreaks occurred (©) or neverdefoliated by the gypsy moth (�).

predators are more abundant where the vegetation undergrowthis well developed (Luciano et al., 2002; Muzika et al., 2004).No correlation was observed between the relative frequencyof outbreaks and exposure or soil type, perhaps as a result ofthe adaptability of the cork oak, whose biomass production andthus quality of food provided to the gypsy moth are not affectedby these factors (Dettori et al., 2001).

Visual inspection of the temporal pattern of gypsy mothoutbreaks in Sardinia showed similarities with oscillations ingypsy moth populations in 11 forested areas in the northernhemisphere, indicating a periodicity of 8–12 years (Johnsonet al., 2005). From 1990, the relative frequency of outbreaksincreased in some oak forests, whereas the oscillation did notvary in other districts. These patterns were supported by theanalysis of local population dynamics, as highlighted in districts1 and 2, both of which have predominant cork oak coverage.

Table 3 Determination coefficients (r2) of the semivariogram modelsused for kriging interpolations and cross-semivariogram models withelevation, relative frequency of outbreaks, or slope as covariates usedfor cokriging interpolations

Cokriging

Year Kriging ElevationaRelative frequencyof outbreaksa Slopea

1980 0.06NS 0.05NS 0.49∗ 0.00NS

1981 0.18NS 0.25∗ 0.60∗ 0.30∗

1982 0.00NS 0.23∗ 0.36∗ 0.26∗

1983 0.05NS 0.27∗ 0.44∗ 0.05NS

1984 0.57∗ 0.83∗ 0.75∗ 0.78∗

1985 0.78∗ 0.25∗ 0.79∗ 0.67∗

1986 0.81∗ 0.34∗ 0.70∗ 0.79∗

1987 0.77∗ 0.39∗ 0.79∗ 0.85∗

1988 0.63∗ 0.49∗ 0.85∗ 0.84∗

1989 0.83∗ 0.72∗ 0.92∗ 0.84∗

1990 0.88∗ 0.66∗ 0.93∗ 0.85∗

1991 0.84∗ 0.36∗ 0.81∗ 0.72∗

1992 0.90∗ 0.81∗ 0.80∗ 0.82∗

1993 0.54∗ 0.66∗ 0.87∗ 0.84∗

1994 0.63∗ 0.71∗ 0.79∗ 0.89∗

1995 0.62∗ 0.75∗ 0.88∗ 0.90∗

1996 0.65∗ 0.71∗ 0.92∗ 0.90∗

1997 0.87∗ 0.94∗ 0.94∗ 0.95∗

1998 0.88∗ 0.81∗ 0.94∗ 0.88∗

1999 0.87∗ 0.91∗ 0.90∗ 0.84∗

2000 0.76∗ 0.77∗ 0.86∗ 0.89∗

2001 0.58∗ 0.39∗ 0.68∗ 0.69∗

2002 0.50∗ 0.26∗ 0.58∗ 0.56∗

2003 0.75∗ 0.92∗ 0.75∗ 0.77∗

2004 0.48∗ 0.80∗ 0.76∗ 0.61∗

aCovariates used to generate the cross-semivariograms prior tocokriging interpolations.∗P < 0.01.NS, not significant.

In North America, gypsy moth outbreak occurrence is relatedto mast seeding failure in oak forests (Liebhold et al., 2000).The unavailability of mast during winter reduces the popu-lation density of small mammals that are the main preda-tors of low-density gypsy moth populations. A similar roleof rodents has been hypothesized in Austria and Japan (Lieb-hold et al., 1998a; Gschwantner et al., 2002). Studies con-ducted in Sardinia on the natural limiting factors of gypsymoth populations showed the importance of Apanteles por-thetriae (Muesebeek) (Hymenoptera: Braconidae) and Exoristalarvarum (L.) (Diptera: Tachinidae) when the gypsy moth den-sity is low, and of Blepharipa pratensis (Meigen) (Diptera:Tachinidae) during the culmination and retrogradation phases(Luciano & Prota, 1986). The minor role played by small mam-mals in Sardinia could be a result of the different behaviour ofthe gypsy moth. In North America, fourth-, fifth- and sixth-instar larvae move from the foliage to daytime resting sites,usually under the bark or in the litter at the tree base, and pupatein the same locations (Campbell et al., 1975). In Sardinia, lar-vae move toward branches or trunk and usually pupate on thehost tree, thus being less exposed to predation by small mam-mals (Luciano et al., 1982). In addition, Sardinian oak stands

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Gypsy moth spatial fluctuation in Sardinia 423

Table 4 Cross-semivariogram models with relative frequency ofoutbreaks as covariate used for cokriging interpolations and modelparameters for the spatial distribution of gypsy moth egg masses inSardinia (1980–2004)

Year Model Nugget Sill Range r2 C/(C0 + C)

1980 Gaussian 0.000 0.0948 18 532 0.49 0.9961981 Gaussian 0.000 0.1322 39 144 0.60 0.9991982 Gaussian 0.000 0.0652 20 611 0.36 0.9981983 Spherical 0.006 0.0385 43 400 0.44 0.8391984 Gaussian 0.000 0.061 32 389 0.75 0.9981985 Spherical 0.001 0.018 42 800 0.80 0.9511986 Spherical 0.003 0.037 37 400 0.71 0.9101987 Spherical 0.002 0.099 35 200 0.80 0.9771988 Spherical 0.002 0.127 33 500 0.85 0.9831989 Exponential 0.001 0.173 45 000 0.93 0.9991990 Exponential 0.000 0.160 52 200 0.93 0.9991991 Spherical 0.008 0.087 32 400 0.81 0.8971992 Exponential 0.000 0.071 53 100 0.81 0.9991993 Gaussian 0.000 0.059 25 461 0.89 0.9981994 Gaussian 0.000 0.118 24 421 0.79 0.9981995 Gaussian 0.001 0.105 22 863 0.89 0.9071996 Spherical 0.001 0.145 29 700 0.92 0.9931997 Exponential 0.013 0.173 53 100 0.94 0.9281998 Spherical 0.002 0.151 32 900 0.95 0.9841999 Spherical 0.003 0.121 34 900 0.90 0.9792000 Spherical 0.001 0.117 30 300 0.86 0.9922001 Spherical 0.001 0.136 30 600 0.68 0.9902002 Spherical 0.001 0.095 29 800 0.58 0.9862003 Spherical 0.006 0.074 38 000 0.75 0.9172004 Spherical 0.000 0.104 46 200 0.76 0.999

Table 5 Cross-validation coefficients of cokriging spatial interpolationswith relative frequency of outbreaks as covariate

Year Cross-validation (r2) Year Cross-validation (r2)

1980 0.30∗ 1993 0.38∗

1981 0.28∗ 1994 0.48∗

1982 0.15NS 1995 0.36∗

1983 0.01NS 1996 0.60∗

1984 0.84∗ 1997 0.62∗

1985 0.27∗ 1998 0.47∗

1986 0.33∗ 1999 0.51∗

1987 0.58∗ 2000 0.53∗

1988 0.48∗ 2001 0.48∗

1989 0.58∗ 2002 0.58∗

1990 0.65∗ 2003 0.24∗

1991 0.48∗ 2004 0.05NS

1992 0.61∗

∗P < 0.01.NS, not significant.

are mostly subject to anthropogenic influence and poor in litter(A. Cocco, personal observation); hence, rodents might find lit-tle suitable habitats for multiplication. No specific observations,however, have been carried out to evaluate the role of smallmammals on gypsy moth predation in Sardinia. Studies con-ducted in Spain, Portugal, Morocco and Italy appear to indicateforest management strategies and predominant host tree speciesas the main factors affecting gypsy moth outbreaks (El Yousfi,

1995; De Sousa, 1995; Bakry et al., 1999; Luciano et al., 1999,2002; Costa & Pereira, 2008; Jurado & Benitez, 2008).

In Sardinia, oak stands characterized by intense grazingare exposed to outbreaks every 5–6 years. The degradationof forest ecosystems might reduce the presence of alternativehosts for multivoltine and polyphagous gypsy moth parasitoidsthat are important in maintaining the pest in latency (Luciano& Prota, 1995). Instead, forests with a well preservedMediterranean maquis allow the proliferation of gypsy mothparasitoids and keep the pest in the latency phase forlonger periods, exhibiting peaks every 8–9 years (Luciano &Prota, 1986). District 2 exhibits a transition from outbreaksevery 8–9 years (1980–1990) to infestations every 5–6 years(1990–2001). This could be a result of the increase of sheepbreeding in these oak stands from the beginning of the 1990sonward (Luciano et al., 2002).

The bioclimatic model identified extensive forested areaswith climatic conditions favourable to gypsy moth outbreaksand thus potentially exposed to defoliations. The climaticrequirements of the pest are met in almost all climatic zones ofSardinia. The presence of 92 monitoring sites where outbreaksnever occurred within the outbreak probability areas suggeststhat the climate in Sardinia is not generally a determinant factorfor gypsy moth outbreaks. Low winter temperatures causedoccasionally high egg mortality, particularly in conditions ofintense cold (Leonard, 1972), whereas Andresen et al. (2001)demonstrated that egg survival is drastically reduced by hightemperatures and varies in relation to solar radiation. Thetypical Mediterranean climate of Sardinia does not considerablylimit the survival of gypsy moth eggs on account of the absenceof extreme maximum and minimum temperatures. The habitatsidentified by the model should be considered the broadestpotential maximum distribution of the gypsy moth becausethe model does not take into account the biotic features ofthe monitoring sites. Georeferenced information (e.g. naturalvegetation and human interventions) might be incorporated inthe model to refine the analysis of the climate influence ongypsy moth outbreaks.

The geostatistical analyses revealed different populationdynamics and magnitude of the outbreaks in the variousdistricts. The spatial interpolations of the gypsy moth eggmasses showed a spatial correlation within a mean ± SE rangeof 35.3 ± 1.9 km, which confirms the validity of the decision tolocate the monitoring sites at a mean distance of approximately5 km inside each district (Luciano, 1989). The nugget valuesindicated the absence of variance at a scale lower than theminimum distance between monitoring sites. Moreover, thehigh values of C/(C0 + C) ratio suggested strong dependencebetween data pairs and their distance.

The results obtained in the present study suggest the use ofGIS technologies as predictive and land use tools to managegypsy moth outbreaks in Sardinian cork oak forests. Mapshave shown the distribution of the pest at a regional scale,providing early information about the areas exposed to gypsymoth outbreaks where the monitoring site network should beintensified to delimit the treatment areas at a district level.In addition, the bioclimatic model described here suggeststhe existence in Sardinia of environmental conditions suitablefor the adaptation of gypsy moth. This statistical approach

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424 A. Cocco et al.

Figure 6 Spatial distribution of gypsy moth egg masses from 1986–1993. Maps were generated by cokriging with relative frequency of outbreaks asa covariate.

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Gypsy moth spatial fluctuation in Sardinia 425

provides a useful tool of technical assistance to predict thepotential distribution of gypsy moth and the areas prone toattack. Additional studies on the influence of environmentalfactors on the periodicity of outbreaks would provide a betterunderstanding of spatio–temporal variations of gypsy mothpopulations in Sardinia.

Acknowledgements

The authors thank Mario Deroma of the University ofSassari, Department of Territorial Engineering, for providingpedological data of the monitoring sites and three anonymousreferees for their constructive comments that greatly improvedthe manuscript.

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Accepted 5 April 2010First published online 7 June 2010

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Agricultural and Forest Entomology © 2010 The Royal Entomological Society, Agricultural and Forest Entomology, 12, 417–426