Spatial, bottom-up, and top-down effects on the abundanceof a leaf miner

Tatiana Cornelissen and Peter Stiling

T. Cornelissen (tgcornel@mail.usf.edu), Dept of Biology, Univ. Federal de Uberlandia, Campus do Pontal, 38302-000, Ituiutaba, MG, Brazil.� P. Stiling, Dept of Biology SCA 110, Univ. of South Florida, 4202 E. Fowler Av., Tampa, FL 33620-5150, USA.

Many plant and animal species have higher densities at the centre of their distribution, with a gradual decline inabundance towards the edge of the range, though reasons for this pattern is not well known. We examined the abundanceof the leaf miner Cameraria sp. nova over the range of its host plant Quercus myrtifolia in Florida and addressed howbottom-up and top-down factors varied over its whole distribution. Leaf miner densities, plant quality and natural enemyeffects on mine survivorship were evaluated in 40 sites and spatially structured models were used to determine the effectsof spatial location on the abundance of Cameraria and effects of both bottom-up (tannin concentration, foliar nitrogen,soil nitrogen, and leaf area) and top-down factors (larval parasitism and predation) on abundance and survivorship.Cameraria mines were, on average, three times more abundant on edge/coastal sites compared to centre/inland sites anddid not support the hypothesis of higher abundance on the centre of the distribution. Differences in plant quality, larvalparasitism and successful emergence of mines on edge versus central sites might be partially responsible for this finding. Atrend surface equation with latitude and longitude combined explained almost 52% of the variation in Cameraria densityand a trend surface map also revealed peaks of Cameraria abundance on the edges of the plant distribution. Correlogramsalso indicated a significant spatial structure of Cameraria as mines were positively spatially autocorrelated at smalldistances (:122 km). Partial regression analyses indicated that 69% of the variation in Cameraria abundance wasexplained by the effects of latitude, longitude, elevation and percentage of foliar nitrogen. Our results indicated thatvariation in Cameraria abundance was mostly explained by spatial position and significant effects of bottom-up and top-down factors were not detected in our large-scale study.

It has been widely demonstrated that species tend to vary inabundance across their distributional ranges: many plantand animal species have higher densities at the centre oftheir distribution, with a gradual decline in abundancetowards the edge of the range (Whittaker 1971, Hengeveldand Haeck 1982, Brown 1984, Curnutt et al. 1996,Mehlman 1997, Sorte and Hofmann 2004). Many biogeo-graphy texts have described this ‘‘abundant centre’’ dis-tribution and it has even been called a ‘‘general rule’’ ofbiogeography (Sagarin and Gaines 2002). Several mechan-isms have been proposed to explain the abundant centredistribution, and most of them are variations of the ideathat species abundance distributions are coupled to envir-onmental gradients. According to Brown (1984), habitatquality for a species is determined by a combination ofmany abiotic and biotic factors and local abundance is areflection of how well a particular site meets the needs of aspecies along many niche axes. These axes include thephysiological characteristics of the species (e.g. temperaturetolerance, soil quality) as well as ecological factors (e.g.response to competitors and mortality imposed by predatorsand parasites). Brown (1984) and Brown et al. (1995)

assumed that these parameters are spatially autocorrelatedand increasing the distance from an optimal site decreasesthe probability of a site meeting the multidimensional needsof a species, with a consequent decline in populationabundance. Exceptions for the abundant-centre distributionhave been found for North American trees, which follow an‘‘abundant-core’’ distribution (sensu Murphy et al. 2006),and even higher abundance on the edges of the distributionhas been found for several plant and animal species (Princeet al. 1985, Blackburn et al. 1999). Also, among the 22studies reviewed by Sagarin and Gaines (2002) that directlyaddressed the abundant centre distribution hypothesis, only8 studies included data collected throughout the entirerange of the species, raising concerns as to how well otherstudies reflect the patterns of change in local abundanceacross entire geographical ranges (Brewer and Gaston2002). Little is known about the relationship betweenspatial variation and abundance of insects throughout thehost plant range, except for the extensive studies conductedfor the holly leaf mining Phytomiza ilicis (Diptera:Agromyzidae) in Europe (Brewer and Gaston 2002, 2003,Klok et al. 2003, Gaston et al. 2004). However, leaf mining

Ecography 32: 459�467, 2009

doi: 10.1111/j.1600-0587.2008.05590.x

# 2009 The Authors. Journal compilation # 2009 Ecography

Subject Editor: Jens�Christian Svenning. Accepted 3 September 2008

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insects, in particular, can be ideal models to address theabundant centre distribution hypothesis: they are generallyrestricted to a single host-plant species and their sessile habitfacilitates sampling and the collection of life-history data inthe field.

The range edges of the host plant species are supposed tobe subject to the most stressful conditions because of waterdeficit and/or nutrient imbalances that affect host plantphysiology and quality (Lawton 1993, Siikamaki andLamni 1998). The plant stress hypothesis argues thatherbivore abundance is higher on stressed host plants dueto an increased availability of nutrients, a decreasedconcentration of defensive compounds, and changes inthe ratio of nutrients to chemical defenses (White 1984). Ina recent meta-analysis of the effects of water-stress on insectherbivores, Huberty and Denno (2004) found that stressedplants showed a tendency to exhibit elevated foliar nitrogenand there was a non-significant trend for leaf miners(mainly Lepidoptera and Diptera) to achieve higherdensities on stressed plants over control plants. As arguedby Huberty and Denno (2004) leaf miners are thought tobenefit from water-stressed plants because they can takeadvantage of elevated leaf nitrogen and yet avoiding feedingon compartmentalized allelochemicals, and thus benefitfrom elevated nitrogen arising from intermittent stress. Inthis sense, stressed plants at the edges of the range might bemore heavily attacked and exhibit larger populations ofinsect herbivores. Also, herbivores might develop faster instressed plants, in accordance with predictions of the slow-growth, high-mortality hypothesis (Clancy and Price 1987),thus decreasing the mortality pressure exerted by naturalenemies, such as predators and parasitoids.

How effects of plant quality and natural enemies interactto determine herbivore abundance and performance is wellunderstood, but virtually all efforts to quantify the relativecontributions of top-down and bottom-up regulatoryfactors on herbivore populations have been performedover relatively local scales, with very few exceptions (Brewerand Gaston 2002, Gaston et al. 2004). Plant qualityfeatures such as the concentration of secondary compoundsas well as the abundance and composition of natural enemycommunities might vary according to environmental factorssuch as soil type, temperature and rainfall patterns. As aconsequence, both the quality of host plants and thepressure exerted by the third trophic level might differbetween different parts of the distributional range of thespecies (Gaston et al. 2004).

Previous studies of oak leaf miners have revealed highvariation in density both between and within plants (Sato1991, Brown et al. 1997) and similar results were found forscrub oaks in Florida at local spatial scales (Stiling et al.1987, Cornelissen and Stiling 2005, 2006a, b, 2008). Inthis study we investigated the abundance and survivorshipof the leaf-mining moth Cameraria sp. nova throughout therange of its host plant, Quercus myrtifolia (Fagaceae). Thissystem provides an ideal model to test the abundant centrehypothesis and the effects of top-down and bottom-upfactors at large scales: the sessile larva is easily identified bythe shape of the mine, and mines are apparent and leave apermanent record of larval fate, providing insight on top-down effects. Moreover, Q. myrtifolia has a geographicrange 99.8% contained within the state of Florida (Nixon

1997, Price et al. 2004). This enabled us to sample the fulldistributional range of Cameraria mines and contrastabundance, survivorship, and demographic features of thisleaf miner in all parts of its range.

Study system

Quercus myrtifolia (Fagaceae) is a semi-deciduous oak thatoften grows in dense stands in Florida scrub ecosystems ondry, sandy soils, usually associated with sand dunes or sandhill (Wunderlin and Hansen 2000). It is also present insmall, patchily distributed stands in Alabama, Georgia andSouth Carolina (Nixon 1997), but most of the distributionof Q. myrtifolia is in Florida, where the plant is present in ca44 (out of 67) counties, except in the northern edge-of-range (Wunderlin and Hansen 2000).

Cameraria sp. nova (Lepidoptera: Gracillariidae) is themost common leaf miner on Q. myrtifolia and mines areabundant in late spring and early summer. Mating,oviposition and egg hatch occur between April and Mayand the upper surface blotch mines appear on Q. myrtifolialeaves by mid-May and early-June. Mines can reach 4 cm inlength and occupy 50�80% of the upper leaf surface(Cornelissen unpubl.). The larvae go through 5 instars,taking up to 4 weeks to complete development. Pupae formwithin a silken cocoon inside the mine. Although Camera-ria sp. nova is the most common leaf miner on Q.myrtifolia, similar mines occur in low densities on chapmanoak Q. chapmanii and in very low densities on the sand liveoak Q. geminata, though whether they belong to the samespecies is not known. However, we suggest that Cameraria’sgeographical range is primarily limited by the availability ofmyrtle oaks, as densities on both chapman and sand liveoaks are extremely low (average9SD number of Camerariamines on Q. chapmanii 0.05890.06 per 200 leaves and onQ. geminata 0.01590.02 per 200 leaves).

Methods

Data collection

Between 1 July and 5 September 2005, data on theabundance and demographic parameters of Cameraria sp.nova, as well as data on aspects of plant quality, weredetermined at 40 sites in Florida, encompassing most ofthe distribution of Q. myrtifolia, its primary host. Sitesincluded state parks, state forests, and reserves (Supplemen-tary material), and sites were selected based on the presenceof pure scrub patches, consisting mainly of Q. myrtifolia,Q. chapmanii, Q. geminata, and Q. inopina. In a few sites(3 out of 40) turkey oak Quercus laevis was also present, butnot sampled in this study. Plants were sampled in 36Florida counties, covering ca 170 000 km2. Plants were notsampled in 23 counties in Florida, as Wunderlin andHansen (2000) list this species as absent in 18 counties inthe northern part of the Florida Panhandle and absent from5 other counties in the central and southern portions of thestate. At each site, we recorded geographic position(latitude-longitude coordinates expressed as decimal de-grees) and elevation using a GPS positioned where the first

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individual of Q. myrtifolia was found. Coastal sites wereB1 km from shore, whereas inland sites were at least 10 kmfrom shore.

Leaf miner density was estimated at each site bycounting the number of Cameraria sp. nova mines on200 randomly chosen leaves of 15 Q. myrtifolia individualplants per site (n�3000 leaves per site). Leaves weresampled randomly, all around the canopy, and leaf minerswere identified and recorded. We also recorded theabundance of other leaf miners (mainly Stigmella sp.,Bucculatrix sp., Stilbosis sp. and Brachys sp.) and otherherbivores belonging to several guilds, such as stem-gallers,leaf-gallers, leaf-tiers, chewers and leaf rollers.

Based on previous knowledge on other scrub oak-leafminer systems (Simberloff and Stiling 1987, Cornelissenand Stiling 2005, 2006a, b, 2008) we evaluated the mostrelevant bottom-up and top-down factors that couldpotentially influence the abundance of Cameraria minesover the range of its host plant. Differences in plant qualitywere assessed by collecting 50 undamaged leaves (collectedfrom at least 10 trees) on each site. Leaves were immediatelyplaced on ice, returned to the laboratory and dried in anoven at 50oC for 3�4 d. Digital pictures of all leaves weretaken in the lab to estimate mean leaf area of Q. myrtifoliaon each site. Leaves were then milled to a fine powder andtannin and foliar nitrogen concentration were determinedfrom 10 leaves per site. Tannins were extracted from 50 mg

of dry tissue, and tannin concentration was quantified usingthe radial diffusion assay with three replicates per leaf (fordetails see Hagerman 1987). Nitrogen content was deter-mined using a CE Instruments NC2100 CN Analyzer (CEElantech, Incorporated, Lakewood, NJ, USA). We alsocollected 3 soil samples at each site, by taking 3 randomcores near 3 different plants and sampling soil 5�10 cmdeep. Soil samples were analyzed for percent nitrogen andcarbon using the same CHN Analyzer.

At each site we collected between 50 and 100 minedleaves to assess survivorship rates and sources of mortality(n�3026 mines collected). Successfully emerged larvae ofCameraria exit the mine as flying adults by cutting the edgesof the silky cocoon. Mines that were preyed upon areusually found open and the larva is missing. Larvalparasitism is easily recognized by the presence of parasitoidsinside the mine, by the presence of parasitoid holes on theexternal surface of mines and by the presence of parasitoidpupae attached to the leaf miner larvae. Larvae killed byhost plant resistance are usually found dead inside the mine,but intact and flat, and mine walls are usually blackened(Simberloff and Stiling 1987). Demographic rates for thisparticular leaf miner consist only of larval survivorship andmortality data, because assessment of oviposition forCameraria sp. nova is not easily done, as no scar is leftafter female oviposition and eggs were no longer foundwhen censuses started.

Figure 1. Distribution of Cameraria sp. nova mines on 40 sites sampled in scrub patches in Florida. The size of symbols is proportionalto the abundance of Cameraria on each site.

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Data analysis

To determine whether local population densities of Cam-eraria exhibit spatial structure across the range of Q.myrtifolia, spatial correlation analysis was performed usingthe mean density of Cameraria mines at each site on Q.myrtifolia. Spatial structure was determined by calculatingMoran’s I spatial autocorrelation statistics as:

I(d)�

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where yh’s and yi’s are the values of the observed variables atsites h and i. Before computing spatial autocorrelationcoefficients, a matrix of geographic distances D�[Dhi] wascreated using Geodesic distances, i.e. distances that takeinto account the curvature of the earth’s surface. Distancevalues were then grouped into distance classes followingSturge’s rule (n classes�1�3.3logn, Legendre and Le-gendre 1998) and Moran’s I was calculated for 7 equallyspaced classes. Spatial correlograms were then created byplotting autocorrelation coefficients for the various distanceclasses d. The weights whi in the above equation areKronecker deltas (Legendre and Legendre 1998) of a binaryform, i.e. the weights take the value whi�1 when sites hand i are at distance d (same class) and whi�0 when sites hand I are at different classes. Finally, W is the sum of theweights whi for the given distance class, i.e. the number ofpairs used to calculate the coefficient. Moran’s I usuallytakes values in the interval [�1,�1] and positive auto-correlation in the data translate into positive values of I andnegative autocorrelation produces negative values (Legendreand Legendre 1998). Spatial autocorrelation coefficientswere tested for significance by calculating confidenceintervals and the significance of the overall correlogramswere assessed after Bonferroni’s correction for multiplecomparisons.

Because plant quality and mortality rates might showspatial structure, i.e. closer sites are more similar than moredistant sites, we also used Moran’s I to describe spatialstructure in bottom-up (tannin concentration, foliar nitro-gen, soil nitrogen and leaf area) and top-down (mortalityimposed by predators and mortality imposed by parasitoids)factors. All analyses were conducted using PASSAGE ver.1.0 (Rosenberg 2001).

The effects of biotic (bottom-up and top-down factors)and spatial variables (latitude, longitude, and elevation) onthe abundance of Cameraria over the range of Q. myrtifoliain Florida were evaluated using trend-surface analysisfollowed by partial regression analyses. Following Legendreand Legendre (1998), Brewer and Gaston (2002) andMcGeoch and Price (2004) we used partial regressionanalysis to estimate how much variation in Camerariaabundance can be attributed to bottom-up and top-downfactors once the effects of spatial location have been takeninto account. We first applied trend-surface analysis todetermine which combination of spatial variables (latitude,longitude, elevation) best explained the variation inCameraria abundance over the range, and a third-degree

polynomial equation was used after centering the values oflatitude and longitude on their means. Only significantterms after stepwise backward multiple regression wereretained to be used in further analyses. Examination ofresiduals using Moran’s I statistics indicated that the trend-surface model extracted all the spatially structured variationof the data, as the correlogram of residuals was non-significant (p�0.05). Partial regression analyses were thenused, in which independent variables were all the spatialvariables addressed in this study (best-fit trend surfacemodel variables) as well as biotic variables studied (allbottom-up and top-down factors) and the dependentvariable was the abundance of Cameraria in each of the40 sites. The combined effects of both top-down/bottom-up and spatial variables on Cameraria abundance over therange were calculated by a series of multiple regressions ofCameraria abundance against biotic and spatial parametersthat were significant after trend-surface analysis (Legendreand Legendre 1998). All residuals were saved and threemultiple regressions of 1) Cameraria against all biotic andspatial variables (a�b�c in the partial regression model,see explanation below), 2) Cameraria against biotic variables(a�b in partial regression model) and 3) Cameraria againstspatial variables (b�c in partial regression model) wereperformed using Systat 9.0 for Windows. These multipleregressions allowed us to partition variation in Cameraria

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Figure 2. (A) Spatial correlogram of the abundance of Camerariamines from across the geographical range of Q. myrtifolia. Solidcircles identify autocorrelation statistics that remain significantafter progressive Bonferroni’s correction (a�0.05) and emptycircles are non-significant values. Data shown with standarddeviation of Moran’s I. (B) Number of pairs of comparisons foreach distance class used for the autocorrelation analysis.

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abundance into 4 fractions (a, b, c, d) representing: a) non-spatial environment (fraction that can be explained bybottom-up and top-down factors that are independent ofspatial structure); b) spatially structured environmentalvariables (spatial structure shared by Cameraria and bioticvariables measured); c) non-environmental spatial (spatialstructure in Cameraria densities not explained by themeasured biotic variables), and d) unexplained residualvariation. Significance of fractions a and c was tested usingF-ratio tests (Legendre and Legendre 1998, McGeoch andPrice 2004).

Results

Cameraria mines were found in all 40 sites sampled inFlorida (Supplementary material), although large variationswere observed at larger scales: mines were, on average, threetimes more abundant on coastal compared to inland sites(mean number of Cameraria mines on coastal sites 99.198.99, inland sites 33.6594.99, F1,38�41.58, pB0.00001,Fig. 1). As a consequence of Florida’s peninsular character-istic, sites on the edges of the range of Q. myrtifoliacoincided with most coastal sites and exhibited significantly

higher numbers of mines than sites at the centre of thedistribution of the host plant (mean number of Camerariamines on edge sites 95.99 8.07, centre sites 26.2392.84,F1,38�53.27, pB0.00001).

Correlograms constructed for the abundance dataindicated a significant spatial structure of Cameraria minesover the range: mines were positively spatially autocorre-lated at small distances (:122 km), indicating that sitesclose to each other tend to have similar mine densitiescompared to sites further apart (pB0.05 after Bonferroni’scorrection, Fig. 2). The size of the patches, i.e. the distancebetween zones of high and low densities, is indicated in thecorrelogram by the distance in which the first negativeautocorrelation was found, at ca 487 km. A significantpositive autocorrelation was also observed between sitesseparated by longest distance (:850 km), which mightrepresent coastal sites at opposite edges of the geographicalrange. No spatial structure was detected for the bottom-up(Fig. 3) or top-down factors (Fig. 4). The only demographicparameter that showed spatial structure was successfulemergence of larvae (Fig. 4), indicating that sites near toeach other exhibited similar levels of mine survivorship. Atvery long distances, successful emergence was also negativelyspatially autocorrelated, indicating that sites very far apart

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122.0 244.0 366.0 487.9 609.9 731.9 854.0 122.0 244.0 366.0 487.9 609.9 731.9 854.0

122.0 244.0 366.0 487.9 609.9 731.9 854.0122.0 244.0 366.0 487.9 609.9 731.9 854.0

Figure 3. Spatial correlograms of bottom-up factors that might influence Cameraria abundance over the range of Q. myrtifolia. Allcorrelograms were statistically non-significant after Bonferroni’s correction (all p�0.05).

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are dissimilar, although these results should be interpretedwith caution, as in the last distance classes Moran’s I iscalculated from a relatively low number of pairs of sites (9in this case). Successful emergence showed a similar patternof autocorrelation to Cameraria abundances at smalldistances, i.e. sites that were apart in:122 km were moresimilar in the number of mines and rates of mine survivor-ship than sites farther apart.

A trend surface equation explained 51.4% of thevariation in Cameraria by a combination of latitude (x)and longitude (y) with 5 significant terms (y�47.45�22.92x�26.05y�30.39xy�27.59y2�3.15y3, r2�0.514,F5,33�6.971, pB0.001) and a trend surface map alsorevealed peaks of Cameraria abundance on the edges of thedistribution of Q. myrtifolia (Fig. 5). Regression analysesindicated that mainly spatial variables contributed tovariation in the density of Cameraria mines over the range:68.5% of the variation in Cameraria abundance wasexplained by the combined effects of latitude, longitude,elevation, and percentage of foliar nitrogen (r2�0.685,F13,25�4.175, pB0.0001, Supplementary material). Par-tial regression analyses using the significant terms revealedthat, once spatial position was taken into account (fraction c

on model), only 6.8% of the variation in Camerariaabundance could be attributed to biotic variation aloneand this fraction was not significant (fraction a on model:r2�0.068, F7,31�0.325, p�0.05). The combined effectsof biotic variables and spatial position (fraction b) explained24.9% of the variation in Cameraria abundance over therange. These results indicated that variation in Camerariaabundance could be mostly explained by spatial position(Supplementary material) and significant effects of bottom-up and top-down factors were not detected in our large-scale study.

Discussion

Spatial patterns in the abundance of Cameraria

Almost without exception, individual species exhibit greatspatial variation in the local densities which they attain(Blackburn et al. 1999). They are entirely absent from mostplaces, at relatively low densities in the majority of those atwhich they occur, and at relatively high densities in a smallproportion of occupied sites (Blackburn et al. 1999). Space

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122.0 244.0 366.0 487.9 609.9 731.9 854.0 122.0 244.0 366.0 487.9 609.9 731.9 854.0

122.0 244.0 366.0 487.9 609.9 731.9 854.0122.0 244.0 366.0 487.9 609.9 731.9 854.0

Figure 4. Spatial correlograms of demographic rates and top-down factors that might influence Cameraria abundance over the range ofQ. myrtifolia. Successful emergence was the only demographic parameter that was significantly spatially structured after Bonferroni’scorrection.

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has been recently recognized as a key factor influencinginsect abundance and distribution (Roslin and Kotze 2005)and a significant spatial structure was observed for Camera-ria mines over the range of Q. myrtifolia. Positive spatialautocorrelations indicated that sites in closest proximity toeach other were similar in both the abundance of mines aswell as successful emergence, an indicator of mine survivor-ship. However, none of the top-down and bottom-upfactors analyzed in this study were spatially structured, asdemonstrated by the non-significant spatial correlograms.Similar results were found by Gaston et al. (2004)addressing variation in quality of Ilex aquifolium leavesover its range in Europe. Values of Moran’s I for that studywere very low and non-significant, indicating little similar-ity in most measures of host-plant quality used. Our datasuggests that variation in mine survivorship (i.e. successfulemergence) across sites might contribute to the variation inCameraria densities over the range. Our analyses of tanninconcentration over the range of Q. myrtifolia indicated thattannins were not spatially structured and did not contributeto the variation in Cameraria abundance. The amounts offoliar nitrogen were also not spatially structured, but didcontribute to a small amount of variation in leaf minerabundance, a finding previously recorded in other plant-herbivore systems (reviewed by Waring and Cobb 1992,Kyto et al. 1996). Genotypic differences among individualsthat are not influenced by spatial position might explainthese results, but were not analyzed in this study.

Bottom-up and top-down influences in theabundance of Cameraria at large scales

The lack of spatial structure in the bottom-up and top-down factors analyzed in our study and the presence ofspatial structure in Cameraria abundance indicates that

there is no simple covariance between these two (Gastonet al. 2004). Although we have shown that there is somevariation in the amount of tannins and foliar nitrogenamong sites, as well as the amount of larval mortalityinflicted by natural enemies, there is no indication that thisvariation shows a distinct spatial structure. While factorsthat influence host-plant quality such as light and soilnutrients might vary with environmental conditions, theremay be no consistent trends in these factors with changes inlatitude and/or longitude (Gaston et al. 2004), as alsoshown in the present study. Bottom-up and top-downfactors analyzed in this study were not spatially structuredand contributed little to the variation in Camerariaabundance over the range of Q. myrtifolia when spatialposition was taken into account, a finding previouslyrecorded for other sessile insects (McGeoch and Price2004, Gaston et al. 2004). In previous studies, we havedemonstrated that leaf miners on oaks respond to bothnatural as well as experimentally elevated levels of foliarnitrogen, and mine mortality tends to be relatively low andnot an important determinant of mine abundance insubsequent seasons (Cornelissen and Stiling 2005, 2006a,b, 2008).

Cameraria abundance and the abundant-centrehypothesis

Contrary to what was predicted by the abundant-centrehypothesis, Cameraria densities did not peak in the centreof the distribution of Q. myrtifolia: higher abundances wereactually observed at the edges of the distribution, coincidingwith coastal sites in Florida. It has been suggested that themost favorable conditions are found at the centre of aspecies’ distribution, and these conditions in turn, supportthe highest population density across the distributionalrange. Our results do not support this hypothesis, as centralsites exhibited lower densities. Brown et al. (1995)suggested that spatial correlograms of species typicallyshow a bowl-shape pattern, with positive spatial autocorre-lations at both short and very long distances, and arguedthat high autocorrelations at very long lags (:850 km inour study) might indicate the similarly low levels of densityfound at opposing range edges. Although the correlogramfor Cameraria shows the typical bowl-shape, we suggest thatstrong and significant positive autocorrelations found at thelargest distance class actually indicates edge sites with highdensity of mines, at the opposite edges of the distribution,such as the Florida Panhandle and the Atlantic coast.

Coastal vs inland populations of Cameraria in Florida

A comparison of plant quality features between central andedge sites (Supplementary material) revealed that edge sitesexhibited significantly higher foliar nitrogen (F1,38�4.834,p�0.034) and a tendency for reduced tannin concentra-tion (F1,38�3.161, p�0.08). These results offer somelimited support to the plant stress hypothesis, and reinforcethe idea that leaf miner populations might benefit fromincreased nitrogen concentration in stressed/coastal plantsand might peak in abundance on the distribution edges,instead of centre or interior populations. Although a recent

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Figure 5. Trend-surface map of estimated Cameraria densitiesbased on latitude and longitude for 40 sites in Florida. Data weremodeled using a third-degree polynomial equation (y�47.45�22.92x�26.05y�30.39xy�27.59y2�3.15y3, r2�0.514; F5,33�6.971, pB0.001), see Methods for further explanation.

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meta-analytical review (Huberty and Denno 2004) showedlimited support for the plant stress hypothesis, leaf minerswere an underrepresented group, and these authors sug-gested that future studies addressing this hypothesis shouldfocus on herbivore guilds for which substantial exampleswere lacking, such as leaf miners and stem borers. Ourstudy provided evidence that, at the largest scale ofdistribution of this particular leaf miner species, plantquality seems to be an important determinant of the spatialstructure of this species.

Organism abundance and fitness may also be affected bybottom-up and top-down factors, which, in turn, might bevariable in edge and centre sites of the distribution of aspecies. Morris (1971) found higher abundances of the fallwebworm Hyphantria cunea on coastal compared to inlandpopulations, and McGeoch and Gaston (2000) foundhigher abundance of the holly leaf miner P. ilicis on edgecompared to interior habitats in England. Explanations forthis last pattern included lower bird predation and pupalparasitism in edge versus interior populations. In thepresent study, parasitism rates of Cameraria larvae oncentre sites were ca 35% higher than parasitism rates onedge sites (mean parasitism centre sites: 21.2%92.5, edgesites: 15.3%91.9), whereas no significant differences wereobserved for mine predation (mean larval predation oncentre sites: 27.9%92.4, edge sites: 25.2%91.2).Although not statistically significant, overall larval mortalitywas higher on centre (62.0%93.2) compared to edgepopulations (57.1%93.3) indicating that demographicparameters that determine species abundance and popula-tion dynamics might differ between peripheral and centralpopulations of Cameraria.

After controlling for spatial location, the amounts offoliar nitrogen were the only statistically significant predictorof Cameraria abundance over the range of its host plant. Theimportance of foliar nitrogen for insect herbivores has beenextensively discussed (Mattson 1980) and higher foliarnitrogen on coastal edge plants, where mine densities werehigher offer additional support for the plant stress hypothesisand its effects on herbivores. Drought stress has been shownin a number of studies to significantly impact the top-downand bottom-up influences on insect herbivores (Mattson andHaack 1987, Fay et al. 1993, Levine and Paige 2004) andcoastal barrier islands in Florida exhibit considerably lowrainfall than mainland areas (US Dept of Interior, FL).Rainfall data obtained for sites close to where mine data werecollected showed that, although rainfall averages for themonths of June and July 2005 did not differ between coastaland inland sites (coastal sites: 6.207 inches90.814, inlandsites: 6.304 inches90.847; p�0.05), total rainfall for theyear 2005 (averaged over all 12 months) did differ betweensite types (coastal sites: 4.143 inches90.131, inland sites:4.795 inches90.127) offering some support to the hypoth-esis that coastal sites are more hygrothermally stressed thaninland sites in Florida.

The abundance structure of a species may often be theoutcome of several processes, such as climate, history,resource quality and biotic interactions, among others(McGeoch and Price 2004). If species have populationdynamics driven strongly by resource quality, then abun-dance structures are likely to be determined by thedistribution of resources across the landscape (Roininen

et al. 1996, McGeoch and Price 2004). Although at localscales it has been demonstrated that leaf miners respondstrongly to bottom-up factors (Moon and Stiling 2004,Cornelissen and Stiling 2006a, b), resources themselveswere not spatially structured in our study, indicating that atlarger spatial scales, other factors might influence theabundance structures found for the Cameraria populationstudied. Host-plant quality and natural enemy effects maybe strong and identifiable only at fine, and not landscape,scales (Bevers and Flather 1999), whereas dispersal abilitiesand other abiotic and/or spatial variables, such as latitudeand altitude, might determine leaf miner abundance andstructure at the landscape scale.

Acknowledgements � This research was supported by the NSF grantDEB 03-15190 and T. Cornelissen was partially supported by theBrazilian National Research Council CNPq through a graduatefellowship (grant number 200064/01-0). We thank Andrey Castrofor his invaluable help in the field and Donna Watkins and theDept of Environmental Protection for granting permission tocollect in Florida’s state parks and preserves (permit 06240510).We also thank Dennis Hardin for granting permission to samplein Florida State Forests.

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