Dispersion Patterns and Habitat Associations of Rufous-Sided Towhees, Common Yellowthroats, and Prairie Warblers in the Southeastern Massachusetts Pine Barrens

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DISPERSION PATTERNS AND HABITAT ASSOCIATIONS OF RUFOUS-SIDED TOWHEES, COMMON YELLOWTHROATS, AND PRAIRIE WARBLERS IN THE SOUTHEASTERN MASSACHUSETTS PINE BARRENS

DAVID C. MORIMOTO • AND FRED E. WASSERMANI 2 •Departmentof Biology,Regis College, 235 WellesleyStreet, Weston, Massachusetts 02193 USA, and 2BiologyDepartment, Boston University, 5 CummingtonStreet, Boston, Massachusetts 02215 USA

AnSTRACr.--Weinvestigated dispersion patterns and habitat associationsof Rufous-sided Towhees (Pipiloerythrophthalmus), Common Yellowthroats (Geothlypistrichas), and Prairie Warblers(Dendroica discolor) in the southeasternMassachusetts pine barrens.All three species were regularly dispersedwithin study sites and among study sites at fine grain sizes, apparently the result of intraspecificcompetition. At increasinglycoarse levels of resolution, however, both Rufous-sidedTowhees and Common Yellowthroats were dispersedrandomly acrosssuccessional stages, whereas Prairie Warblersexhibited a patchy dispersion.Prairie Warblersexhibited stronger relationships between density and habitat, and showed relatively strong habitat associations.They apparently responded to broad-scalepatterns of habitat variation acrosssuccessional stages. Rufous-sided Towhees and CommonYellowthroats had weaker associationsat this scale. We suggestthat the most abundant avian speciesin the pine barrensrespond to habitatvariation at different scalesand point to the need for careful considerationof scalewhen investigatinghabitat relationshipsand interpreting patternsof communitystructure. Received 14 March 1990,accepted I October1990.

DISPERSIONpatterns are the result of many MacMahon 1981, Cox 1987, Getis and Franklin factors operating at different spatial and tem- 1987; but see Simberloff 1979, Cox and Roig poral scales (Wiens 1984, Diamond and Case 1986). Few investigatorshave analyzed the dis- 1986, Holmes et al. 1986). These factors include persion patternsof territorial birds (Sherry and habitat variation, biotic processes(such as in- Holmes 1985). We studied the dispersion pat- traspecific and interspecific competition), abi- terns of Rufous-sidedTowhees (Pipiloerythro- otic factors, landscapecharacteristics, and sto- phthalmus),Common Yellowthroats (Geothypis chastic processes.Because dispersion patterns trichas), and Prairie Warblers (Dendroica discol- mayvary with the scaleat which they areviewed or), the three most common breeding bird spe- (Sherry and Holmes 1985), analysesof disper- ciesin the southeasternMassachusetts pine bar- sion conducted at several levels of resolution rens. For each species we tested the null (grain sizes)can provide insight into the factors hypothesisthat individuals were dispersedran- (e.g. the scaleof habitatselection) that influence domly within (small extent) and among (large individual distribution and abundance (O'Neill extent) study sites. We determined dispersion et al. 1986, Wiens 1989). However, knowledge patterns among study sites at several levels of of the ways in which dispersionpatterns change resolution, or grain sizes (Wiens 1989), to in- with changing grain size by itself is not suffi- vestigatethe ways in which dispersionpatterns cient for drawing conclusionsabout how pro- changedwith changing grain of investigation. cesses such as habitat selection influence the We then examined the variation in vegetation distribution and abundance of individuals. structure and composition, and the habitat as- Therefore, analysesof dispersion must be ac- sociationsof each species.In this way we could companiedby studiesof habitat variation and interpret the scale-dependentdispersion pat- habitat occupancypatterns in order to under- terns in light of the responsesby individuals stand how and whether dispersionpatterns can to habitat variation. be accountedfor by the responsesof individuals to habitat variation. METHODS Historically, quantitative analysesof dispersion have been restricted largely to plants This study was conducted in the pine barrens of (Greig-Smith 1961, Kershaw 1973, Phillips and Plymouth (PlymouthCounty), Bourne,and Mashpee

264 The Auk 108: 264-276. April 1991 April 1991] DispersionPatterns in PineBarrens 265

(BarnstableCounty) in southeasternMassachusetts. were adapted from techniques used in quantitative The pine barrensof Plymouth and Barnstablecoun- plant ecology(Kershaw 1973,Vandermeer 1981).For ties together represent the third largest (>20,000 ha) each transect,we superimposeda seriesof contiguous area of pine barrens remaining in North America quadratson the mappedterritories of a given species, (Cryan 1985),with the towns of Plymouth and Mash- and we counted the number of territory centersper pee holding the largest tracts remaining in Massa- quadrat.The centerof a territory was defined as the chusetts.These pine barrens occur on well-drained, center of the smallest circle that enclosed that terri- sandy,glacial outwash soils and are subjectto periodic tory (Sherry and Holmes 1985).Because we were in- burning. The land is topographicallydiverse (0-35% terestedin how dispersionpatterns changed with the slope),with locallyoccurring boulders and occasional spatialscale of resolution,or grainsize (Kershaw 1973), kettle depressionsand ponds,and outwashplain val- we counted the number of territory centersin quad- leys (Oldale 1976). The vegetation is simple both ratsof five different sizes(representing different grain physiognomicallyand floristically.The dominanttree sizes):0.25 ha (232 quadrats;50 x 50 m), 0.50 ha (116 is pitch pine (Pinusrigida), which formsan open can- quadrats;50 x 100 m), 1.0 ha (58 quadrats;100 x 100 opy. Black oak (Quercusvelutina), white oak (Q. alba), m), 3.5 ha (16 quadrats;100 x 350 m), and 7.0 ha (8 and white pine (P. strobus)occur less frequently. The quadrats;100 x 700 m). The small quadratsof 0.25 shrub layer is very denseand is dominatedby scrub ha and 0.50 ha correspondedto the mean (+SD) sizes oak (Q. ilicifolia)and several ericaceousspecies, in- of individual territories (Rufous-sided Towhees, 0.26 cluding black huckleberry(Gaylussacia baccata), sheep + 0.06 ha, n = 277; Common Yellowthroats, 0.28 + laurel (Kalmiaangustifolia), late low blueberry(Vaccin- 0.06 ha, n = 251; Prairie Warblers, 0.36 + 0.15 ha, n iumvacillans), and early low blueberry(V. angustifoli- = 187), whereas the large quadrats of 7.0 ha corre- urn).The perennial herbswintergreen (GaultheHapro- spondedto the size of individual transects.Therefore, cumbens)and bearberry (Arctostaphylosuva-ursi) at the largestgrain sizewe were effectivelymeasuring dominate the herb layer. dispersionpatterns acrosssuccessional stages. Morisi- Eight 7-9 ha strip transects(100 m wide) that rep- ta's measureof dispersion (Vandermeer 1981), essen- resentedan array of successionalstages from 5 yr to tially a variance to mean ratio of the number of ter- >30 yr postfire were established(Appendix 1). Each ritory centersper quadrat, was calculatedfor each of these transects was located within at least 50 ha of speciesand quadrat size;values of < 1 indicated reg- pine barrens vegetation and at least 200 m from a ular dispersion, values of 1 indicated random disper- border with dissimilar habitat. We usedthe plot-map- sion, and values of > 1 indicated patchy dispersion. ping technique (Emlen 1977, Christman 1984) to cen- To determine the significanceof deviationsfrom ran- susbreeding birds on eachof the eight strip transects dom, Chi-square tests that compared the observed from late May to early July, 1985-1987.Plot sizesof numbersof territory centersper quadratwith a Pois- 7-8 ha have been used extensively to censuspine son distribution (Kershaw 1973) were performed for barrensbirds (Lloyd-Evans 1973,1974; Kerlinger 1984), eachyear of the study.Thus, we were ableto measure and are adequateto estimate densities in this open dispersion patterns at a relatively broad scale (in- habitat, particularly for the most abundant species. cluding all transects;encompassing 58.5 ha) with All censuses were conducted between 0500 and 0800 varying degreesof resolution(from 0.25 to 7.0 ha). EST on days without rain or strong winds. The in- In addition to these measuresof dispersion across formation recordedduring the formal censuseswas transects,dispersion was quantifiedwithin eachtran- supplemented with extensive observations of indi- sectby measuringintraspecific nearest-neighbor dis- viduals made during a study of foraging behavior. tancesfor each species.The distance between neigh- Thus we are confident that the locations and the es- bors was defined as the distance between territory timates of abundance and territory boundaries are centers.In caseswhere nearest-neighbordistances be- accurate. In the present analyseswe concentrateon tween two individuals were reciprocal (22% of all the three most abundant species--Rufous-sidedTo- cases),the distance to the second nearest-neighbor whees, Common Yellowthroats, and Prairie War- was used, which avoided statisticalproblems associ- biers--which comprised49-70% of the breeding bird ated with suchcases (Meagler and Burdick 1980,Sher- total density observedduring the study. ry and Holmes 1985).We performed Student'st-tests The dispersionpatterns of individuals are charac- to determine if the averagenearest-neighbor distance terized as (1) random,in which the location of each was different from that predicted in a random distri- individual is independent of the location of other bution describedusing the formula of Clark and Evans individuals; (2) clumped,or patchy,in which individ- (1954). The ratio of the observedaverage nearest- uals are locatedcloser to one another than expected neighbor distanceto the expectedvalue was com- in a random distribution; or (3) regular,in which in- puted as an index of dispersion (Clark and Evans dividuals are more evenly spacedthan expectedby 1954);values of 1.0 indicatedrandom dispersion, val- chance. ues of < 1.0 indicated patchy dispersion,and values To quantify dispersionpatterns across transects we of > 1.0 indicated regular dispersion.By narrowing usedthe methodsof Sherry and Holmes (1985),which the extentof the studyto that of individual transects, 266 MORIMOTO•D WASSERMAN [Auk,Vol. 108 we reduced the spatial variation in habitat (Wiens ted with each independent variable to identify non- 1989)and were thus able to understandmore clearly, linear relationships. comparedwith the fine-grain analysisincluding all We used discriminantfunction analysis(DFA) to transects,the dispersionpatterns of individuals and determinepatterns of habitatutilization (James1971; their causesat the scaleof local populations. Smith 1977; Noon 1981; Rice et al. 1981, 1983; Wil- We useda technique modified from Jamesand Shu- liams 1981, 1983; Conner et al. 1983; Adler 1985). gart (1970) to sample vegetation on 267 randomly Discriminant function analysiscan be usedto predict located 0.04-ha circular plots from 1985-1987 (Ap- groupmembership by classifyingcases based on val- pendix I). In total, 22 variables (Appendix 2) that ues of the canonicalvariates and to interpret differ- consistedof coverageestimates, measures of vertical encesbetween groups by examiningboth the degree and horizontal structure, and indices of vertical and of statisticalseparation of group meansand the vari- horizontalheterogeneity were measuredon eachplot. ables contributing most to the discrimination be- Coverageestimates were determined by recording tween groups. the frequencyof vegetationcontacts per height in- We recordedthe presenceor absenceof individuals terval (0-0.25 m, 0.25-0.5 m, 0.5-1.5 m, 1.5-3 m, 3-6 on each of the 267 vegetationplots. An individual m, 6-8 m, >8 m) with a 5-m vegetation pole placed was considered to be present if its territory over- at 20 locations(every 2.25 m) alongtwo perpendicular lappedat least50% of a samplingplot. Foreach species lines passingthrough the center of each plot. Litter we performeda two-groupDFA using the PCA-de- depth was also measured at these 20 locations and rived habitat measures(factor scores)to discriminate averagedover the entire plot. Foliageheight diversity plotswhere individualsof a given specieswere pres- (FHD), a measureof vertical heterogeneity,was cal- ent from those where individuals were absent. culated from the number of contactsper height in- Bothstepwise and direct DFAs were conductedfor terval using Hill's (1973) modificationof Simpson's eachspecies. Because the direct analysesyielded con- (1949)diversity index. At the 20 samplingpoints per sistentlylower Wilk's lambdavalues and consistently plot, we determinedthe presenceor absenceof veg- higher percentagesof correctly classifiedcases, we etation in each of the seven height intervals. This used only the results of the direct analyses(Rice et gaveeach point a characteristicfoliage profile. Foliage al. 1983).Interpretation of discriminantfunctions was profile diversity, a measureof horizontal heteroge- based on the correlations of the PCA-derived habitat neity, was calculatedfrom the presence/absencein- measureswith the canonicalvariates, a method pref- formation using the Shannon-Weaverindex (Shan- erable to the examination of discriminant function non and Weaver 1949, Ambuel and Temple 1983). coefficients (Williams 1981, Conner et al. 1983, Mor- We performedprincipal componentsanalysis (PCA) rison 1984). The resultsof the classificationanalyses on the vegetationmeasures for the 267 plots to de- were used to examine the habitat discrimination of termine the major orthogonalpatterns of variation in speciesas reflectedin the percentagesof casescor- the vegetation acrossstudy sites. All variables ex- rectly classified.Classification of caseswas basedon pressed as proportions were arcsine-square root prior probabilities of group membership that were transformed (Sokal and Rohlf 1969). The factor scores determined by the proportion of occupiedplots. for all componentswith eigenvaluesof > 1 were re- Several statistical problems associatedwith DFA tained for analysis, and varimax rotation was em- may arisewhen groupsdiffer substantiallyin sample ployedto enhancetheir interpretability(Norusis 1985). size or when the assumptionof homoscedasticityis Eachprincipal componentwas named after the vari- violated (Williams 1981, 1983; Rice et al. 1983; Mor- able with the highest factor loading on that compo- rison 1984).In thesecases the significancevalues as- nent. The factorscores for eachprincipal component sociatedwith the Chi-squaretransformation of Wilk's were averagedfor eachtransect, and their meansand lambdamay be unreliable.In our work Rufous-sided standarddeviations were plotted in multivariatespace Towbees and Common Yellowthroats were absent in order to depict graphically both the relative po- from relatively few plots.In addition, an examination sitionsof transectsalong the vegetationgradients and of the resultsof Box'sM-tests of homogeneityof vari- the nature and extent of within-transectheteroge- ance-covariancematrices revealed that this assump- neity. tion was violated in two of the three cases.Therefore, Relationshipsbetween bird densities and habitat conclusionsregarding habitat selectivity are basedon characteristicswere examined by linear regression. the resultsof the classificationprocedure rather than Separateregressions to relate the averagedPCA-de- on the potentially unreliable Wilk's lambda and as- rived habitat variables (independent variables) for sociatedsignificance values (Rice et al. 1981, 1983). each transectto bird densities(dependent variables) The classificationapplication of DFA can be useful were performed for each species.Residuals were an- and biologicallyvalid despitethe existenceof biased alyzed by examining normal probability plots and significancevalues (Williams 1981, Rice et al. 1983). plots of unstandardizedresiduals with the indepen- To evaluate habitat selectivity we examined the dent variables.In addition, bird densitieswere plot- percentagesof plots correctlyclassified as occupied, April1991] DispersionPatterns inPine Barrens 267 unoccupied,and the overall percentagesof correctly classifiedplots. When examining classificationre- suits,it is important to take into accountthe classi- 2.5 ficationrates expected by chancealone (Norusis1985), particularly when group sample sizes are unequal 2 (Titus et al. 1984), as was the case for Rufous-sided Towhees and Common Yellowthroats. Therefore, we calculatedCohen's (1960) kappa (K), representingthe average proportion of casescorrectly classifiedafter removingthe effectsof chance,using the formula of Titus et al. (1984). The 95% confidenceintervals (C.I.)

for K were calculatedand the null hypothesisthat K 0.5 was no different from chance was tested by calculat- ing the z statistic. These testswere performed for each of the three categoriesof classification.Kappa values and testsof significance allowed us to evaluate the relative strength of the habitat associationsof each species Fig. 1. Densities (œ+ SD) of the three numerically and the extent to which habitat discrimination dif- dominant breeding bird speciesin the southeastern fered from chanceexpectations. Massachusettspine barrens, 1985-1987. Abbrevia- tions: Rufous-sided Towhee (RSTO), Common Yel- lowthroat (COYE), and Prairie Warbler (PRWA). RESULTS

Rufous-sided Towhees were the most abun- The major component of variation (maximum dant species, followed by Common Yellow- height of vegetation, 25.8%) described a gra- throatsand Prairie Warblers. In each year Prai- dient of increasingcoverage of pitch pines, as- rie Warbler densities were considerablymore sociatedwith increasing height of vegetation, variable among transects than Rufous-sided basalarea, and heterogeneity.The secondcom- Towhees or Common Yellowthroats (Fig. 1). ponent (percentagecover of white oak, 12.1%) Prairie Warbler dispersionpatterns changed describeda gradient of increasingcoverage of in different ways from those of Rufous-sided oak trees. The remaining componentseach ex- Towhees and Common Yellowthroats as grain plained relatively little of the overall variation size increased(Table 1). At the smallestquadrat in vegetation and describedindependent gra- size (0.25 ha), correspondingroughly to the ter- dients in the coveragesof herbs,scrub oak, white ritory sizes of these speciesin this habitat, all pine, black huckleberry, and sheep laurel. specieshad dispersion indices that indicated The two-dimensionalordination of average regular dispersion. Rufous-sidedTowhees and factor scoresfor each transectin the spacede- Common Yellowthroats exhibited patterns of scribed by the first two principal components regular dispersionat quadrat sizesof 0.5 ha and (Fig. 2) revealed that maximum height, basal random dispersionat quadratsizes of 3.5 ha and area, heterogeneity, and pitch-pine coverage 7 ha. In contrast to these two species,Prairie (PC1) varied more or less continuouslyamong Warblers were randomly dispersedat quadrat sites.Transects loading high on this component sizes of 0.5 ha and tended to be patchily dis- also tended to be somewhat more heteroge- persedat quadratsizes of 3.5 ha and, especially, neous.In contrast,oak tree coverage(PC2) did 7.0 ha. Analysesof nearest-neighbordistances not vary continuously among transects. Oak revealed that all three specieswere evenly dis- coveragewas very high and variable on MASH2, persed within all transectsin every year (Table overlapping other transectsonly slightly. The 2). This indicatesthe importanceof intraspecific remaining transectswere clusteredon this com- competition in determining the dispersion of ponent. individuals. It appearsthat the eight transectsrepresented The PCA of habitat measuresfor 267 vege- a rather shallow and nonlinear gradient with tation plots extracted seven factorswith eigen- respectto oak coverage,with only MASH2 con- values of > 1 (Table 3). Together these factors tributing substantially to the variation ex- explained 71.5%of the variation in vegetation. plained by this principal component.Further- 268 MORIMOTOAND WASSERMAN [Auk, Vol. 108

TABLE1. Morisita's dispersion indicesa for the three dominant breeding bird speciesin the southeastern Massachusettspine barrens(Rufous-sided Towhee [RSTO], Common Yellowthroat [COYE], Prairie Warbler [PRWA]).Values • 1 indicatea randomdispersion, values < 1 indicatea regular dispersion,and values > 1 indicatea patchydispersion. Significant deviations from a random (Poisson)distribution are indicated (* = P < 0.05, ** = P < 0.01, *** = P < 0.001).

Quadrat size (number of quadrats) No. of territory centers 0.25 ha (232) 0.5 ha (116) Species 1985 1986 1987 1985 1986 1987 1985 1986 1987 RSTO 122 117 114 0.08*** 0.03*** 0.10'** 0.53*** 0.54*** 0.49*** COYE 110 102 102 0.04*** 0.09*** 0.01'** 0.52** 0.56** 0.43*** PRWA 69 66 59 0.19 0.01' 0.01' 0.76 0.65 0.69 ' M = N • n,(n, - 1)/n(n- 1),where n, = totalnumber of territory centers in a quadrat,n = totalnumber of territory centers, and N z number of quadrats (Vandermeer 1981).

more, although the transectsfell along a rather vealed that a linear model was not appropriate continuousgradient on PC1,average pitch pine for describingthis relationship.Rather, Prairie coverage never exceeded 17% on any transect Warblers decreased in abundance in a curvilin- or 45% on any sampling plot. Therefore, all ear fashion with increasingheight of vegeta- transectshad fairly open canopies. tion, percentcover and basalarea of pitch pines, The 21 linear regressionsof the seven PCA- and vertical and horizontal heterogeneity.Prai- derived habitat measures on densities of the rie Warblers were also less abundant on MASH2, three bird speciesrevealed three (14.3%) sig- which was characterizedby high oak tree cov- nificant relationships (Table 4). The examina- erage.No other systematiclinear or nonlinear tion of residualsand density plots (Fig. 3) for relationshipswere apparent for Prairie War- the significantregression of maximum height biers. These habitat relationshipsare consistent of vegetation on Prairie Warbler densities re- with the findings of other studies of Prairie

TABLE2. Ratios of observed to expected nearest-neighbor distancesfor each transect. All values are >1, indicatingregular distributions. Significance values (all P < 0.001,except where indicated;* = P < 0.05, ß* = P < 0.01) were determined using t-tests.Numbers of territories per 7.0 ha are in parentheses;see Appendix 2 for transectnames and locations.

Rufous-sided Towhee Common Yellowthroat Prairie Warbler

Transect 1985 1986 1987 1985 1986 1987 1985 1986 1987

PCWT 1.73 1.74 2.05 2.04 1.64 1.89 2.09 2.15 1.73 (8) (13) (14) (11) (12) (11) (13) (14) (12) MSSF1 1.82 1.87 1.97 2.05 2.09 1.80 2.09 1.85'* 2.10 (17) (14) (15) (15) (14) (12) (13) (10) (10) MSSF2 a 1.78 1.77 1.94 2.17 1.84 1.88 ------(12) (14) (12) (12) (12) (13) (3) (2) (2) MSSF3 2.21 1.94 1.96 2.20 1.80 2.11 2.06 1.56'* 2.06 (20) (15) (13) (15) (13) (15) (15) (17) (15) PLIM 2.00 1.81 1.89 1.95 1.59 1.96 1.95 1.45' 1.85 (13) (15) (13) (15) (14) (15) (10) (11) (13) OAFB 1.86 1.86 2.15 2.10 1.93 1.99 1.96'* 1.87 1.85 (15) (13) (15) (13) (12) (12) (6) (11) (11) MASH1 • 1.97 1.70 1.88 1.84 1.40' 1.86 2.02 -- -- (14) (12) (12) (8) (12) (10) (6) (4) (3) MASH2 a 1.77 1.94 2.02 2.04 1.92 1.79 1.49' 1.94' -- (22) (21) (17) (14) (18) (16) (8) (5) (2)

Overall mean 59.59 59.77 70.01 68.26 63.09 65.40 73.79 74.34 75.08 +SE 1.19 1.21 1.34 1.40 1.60 1.03 2.23 3.04 1.75

• Nearest-neighbortests could not be performedfor PrairieWarblers on transect3 in all yearsand on transects7 and 8 in someyears because there were fewer than five territory centersin these cases. April 1991] DispersionPatterns in Pine Barrens 269

TABLE I. Extended.

Quadrat size (number of quadrats) 1.0 ha (58) 3.5 ha (16) 7.0 ha (8) 1985 1986 1987 1985 1986 1987 1985 1986 1987

0.80 0.79 0.70* 0.97 0.95 0.92 1.02 0.98 0.97 0.70*** 0.82** 0.68 0.93 0.92 0.93 0.98 0.98 1.00 1.04 0.78 0.89 1.15 1.12 1.30' 1.16' 1.21 1.33'*

Warblers (Lloyd-Evans1973, Nolan 1978, Con- ate for describingthese positive relationships. ner et al. 1983,Kerlinger 1984,Brush 1987). An examinationof the plots of bird densities The analysesof residualsfor the regression with factorscores for oak tree coverage(Fig. 3) of oak tree coverage(WOAK) on Rufous-sided revealedthat no systematiclinear relationships Towhee and Common Yellowthroat densities were apparent and that the regressionresults revealedthat linear modelswere not appropri- were due to the high densitiesof thesebirds

TABLE3. Principalcomponents analysis of vegetationvariables in the southeasternMassachusetts pine barrens.Only factor loadings>0.45 are shown,since others contribute <20% to a component.All P < 0.001; see Appendix 2 for variable descriptions.

MAXHGT WOAK HERB SOAK2 WPINE BHUCK SLAUR Variablecode (PC1) (PC2) (PC3) (PC4) (PC5) (PC6) (PC7) Coverage SOAK1 0.758 SOAK2 0.763 BHUCK 0.764 BLUEB 0.852 SLAUR 0.720 SFERN 0.634 HERB 0.894 SMIL 0.491 PPINEI 0.799 PPINE2 0.781 WPINE 0.833 BOAK 0.821 WOAK 0.831

Structure BASAL 0.758 BASAL3 0.675 DEADBAS 0.542 HITS 0.565 0.492 MAXHGT 0.834 FALL -0.468 LITT 0.635 Heterogeneity FHD 0.688 FPD 0.625 Eigenvalue 5.68 2.66 2.02 1.63 1.50 1.18 1.05 % variance 25.8 12.1 9.2 7.4 6.8 5.4 4.8 Cumulative % 25.8 37.9 47.1 54.5 61.4 66.7 71.5 270 MORIMOTOAND WASSERMAN [Auk,Vol. 108

.5.5

MASH2

SSF1I OAFB

PCWT MSSF.,t

-1-

-t.5 -

-2 -1.5 -tI _0•.5 0I 0.5I 1, 1.5I 2• 2.'5 $I .5.5 PrincipalComponent I Fig. 2. Two-dimensionalordination of transectsin the spacedescribed by the first two vegetationprincipal components.Average factor scoresare plotted with standarddeviations.

on MASH2. Although this transect had much sures with the discriminant axes derived from higher average percent cover of white oaks two-group DFAs are presentedin Table 5. Only (10.2%) and black oaks (9.8%) compared with correlations> 0.45 (all P < 0.001)are given. The other transects,oak coveragewas extremely direction and relative magnitude of the corre- heterogeneous(Fig. 2; coefficientsof variation lations are more important considerationsthan equal 142% for white oak and 95% for black oak significancelevels when evaluating the impor- coverage).Rufous-sided Towhees and Common tance of variables in discriminant analyses Yellowthroatsexhibited no systematiclinear or (Conneret al. 1983).The bestseparation of group nonlinear relationshipswith any of the other means occurred for Prairie Warblers. Prairie PCA-derived habitat parameters. Warblers avoided areas with high coverage, The correlations of PCA-derived habitat mea- height, and basal area of pitch pines and high

TAnLE4. Significant(P < 0.05) linear regressionsof PCA-derivedhabitat measures on bird densities(n = 24 for each species).

Dependent variable Independent Adjusted (density) variable" Slope Intercept t P r2 Rufous-sided Towhees WOAK 0.321 2.11 2.88 0.009 0.24 Common Yellowthroats WOAK 0.209 1.90 2.65 0.015 0.21 Prairie Warbler MAXHGT -0.703 1.25 3.96 0.001 0.39

MAXHGT = maximumheight of vegetation;WOAK = percentagecover of white oak. April 1991] DispersionPatterns in PineBarrens 271

Rufous-sided Towhee

3.5- 3.5

2.5

1.,5-

0.5- 0.5

'd -1-0.5 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Common Yellowthroat

3.5- 3.5

2.5

1- 1

0.5- 0.5 0 I t I -1--0.5 0 0.5 1 1.5 2 2.5 -1-0.5 0 0.5 1 1.5 2 2.5 Prairie Warbler

3.5-

2.5 2 ....' 1.5- ." '.:

1- 1

0.,5- ß ß 0.5

-1-0.5 0 0.5 1 1.5 2 2.5 -1-0.5 0 0.5 1 1.5 2 2.5 Po I PO II

Fig.3. Plotsof birddensities with factorscores for thefirst two principal components of vegetation. coverage of oak trees. These results were con- lowthroatsoccurred on plots with high cover- sistent with our density-habitatanalysis. The age of scruboak (Table 5). Despitethese cor- DFAs revealed relationshipsfor Rufous-sided relations, the associated PCA-derived habitat Towhees and Common Yellowthroats that were measuresexplained only a small percentageof not apparentfrom the density-habitatanalysis. the overallhabitat variation compared with the Rufous-sidedTowhees occurred on plots with variable with which Prairie Warbler densities high coverageof herbsand blackhuckleberry, were correlated (Table 4). and with high litter depth; and Common Yel- The classification (Table 6) of these DFAs re- 272 MOmMOTOAND WASSFaCMAN [Auk,Vol. 108

T^I•I,E5. Significantcorrelations (r > 0.45) of habitat the K valuesfor the classificationof unoccupied measures with the canonical variates derived from plots and for overall classificationwere much two-group discriminantfunction analysis.Princi- lower for Rufous-sided Towhees and Common pal componentsare named after the variable with the highestfactor loading. All P < 0.001;see Ap- Yellowthroatscompared with Prairie Warblers. pendix 2 for variable descriptions. In fact, the chance-corrected classification values for unoccupied plots and overall classifi- Rufous- Common cation for Rufous-sided Towhees indicated that sided Yellow- Prairie Towhee throat Warbler no improvement over chancewas provided by the DFA. The considerable differences between Principal Component observed overall classification rates and the cor- 1 MAXHGT 0.65 2 WOAK 0.45 respondingchance-corrected K values suggest 3 HERB 0.68 that the observed (non-chance-corrected) clas- 4 SOAK2 0.68 sification rates--and therefore group predict- 5 WPINE ability--were highly inflated (Titus et at. 1984) 6 BHUCK 0.46 for Rufous-sided Towhees and Common Yet- 7 SLAUR lowthroats compared with Prairie Warblers. Group Means Thus, while the chance-corrected classification Present 0.07 0.21 -0.45 results seem somewhat low, even for Prairie Absent -0.17 -0.44 0.54 Warblers, they are not nearly as low as those Wilk's Lambda 0.99 0.91 0.80 for Rufous-sided Towhees and Common Yel- P 0.884 0.001 0.000 lowthroats. No. of plots occupied 191 180 145 DISCUSSION

Prairie Warblers exhibited more distinct hab- veated that the percentagesof plots correctly itat associations than Rufous-sided Towhees and classifiedas occupied were greater than ex- Common Yellowthroats, as indicated by their pected by chance(P < 0.01) for Common Yel- patchydistribution among transects at the larg- lowthroats and Prairie Warblers in each cate- er grain sizes considered,the one curvilinear gory: occupied, unoccupied, and overall. The relationshipof densityvariation to habitat vari- chance-correctedclassification of occupiedplots ation, and the relatively strong discrimination was generally high yet somewhat lower than between occupied and unoccupied plots. In non-chance-correctedvalues for all speciesex- contrast, Rufous-sided Towhees and Common cept Rufous-sidedTowhees (Table 6). However, Yellowthroatsexhibited relatively weak habitat

T^•LE6. Classificationresults for two-groupdiscriminant function analyses of 267 vegetationplots. Expected valueswere estimatedfrom classificationtables based on prior probabilities.Kappa (K) values,representing chance-correctedproportions of correctlyclassified cases, are alsogiven with 95%confidence intervals (K _+1.96 SE) and associatedsignificance values derived from z-tests(H0: K = 0; * = P < 0.05,** = P < 0.01, .... P < 0.001).

Percent correct classification No.of Observed Expected occupied Species plots Absent Present Overall Absent Present Overall Rufous-sided 191 0.0 100.0 71.5 0 71.5 71.5 Towhee (0.0 _+ 0.00)a (100.0 _+ 0.00) (0.0 -+ 0.19) *** NS Common 180 19.5 93.9 69.7 3.4 60.3 63.8 Yellow- (0.17 _+0.05) (0.85 _+0.07) (0.16 _+0.15) throat *** *** * Priarie 145 59.0 81.4 71.2 16.9 34.2 51.1 Warbler (0.51 + 0.07) (0.72 + 0.07) (0.41 + 0.11)

' We could not calcuEatez-statistic for Rufous-sidedTowhees becausedivision by 0 is impossible. April 1991] DispersionPatterns in PineBarrens 273

associations,as evidencedby their random dis- grain sizes of the 3.5-ha quadratswas charac- persionsamong transectsat larger grain sizes, terized by fewer quadratswith either 0, 1, 2, or the absenceof systematicdensity-habitat rela- 3 birds than expectedin a randomdistribution. tionships,and the overall lower habitat discrim- Thus, Prairie Warblers were responding to inability. within-transect habitat heterogeneityby con- Thesefindings are consistentwith the natural centrating in preferred areas where they be- historiesof thesespecies. Prairie Warblershave cameevenly dispersed. a smallergeographic range, are more locally It is less clear if Rufous-sided Towhees and distributedwithin this range, and have more CommonYellowthroats responded in a system- restricted habitat preferencesthan either Ru- atic way to the major broad-scalepatterns of fous-sided Towhees or Common Yellowthroats. habitat variation encompassedin our study. The overallextent of this study,encompassing Thesetwo speciespresumably preferred a much an array of postfiresuccessional stages in the greater array of pine barrens habitats and thus pine barrensof southeasternMassachusetts, was exhibitedweaker patterns of habitatoccupancy adequateto capture broad-scalehabitat varia- acrosssuccessional stages. Correspondingly, tion to which Prairie Warblers responded.Prai- they exhibited random dispersionsat even the rie Warblersexhibited relatively clearpatterns large grain sizesof the 3.5-ha quadratsand 7.0- of habitatoccupancy across transects. They re- ha quadrats.The failure to detectstrong habitat spondedto the major componentsof habitat relationships for Rufous-sided Towhees and variation: height, coverage,and basal area of Common Yellowthroats within the pine bar- pitch pines. This habitat variation occurred on rens doesnot mean that habitat preferencesdo a scale commensurate with the one at which not exist. For example, habitat preferences Prairie Warblersexhibited a patchydispersion within the pine barrensmight becomeapparent (i.e.at the relativelycoarse grain size of the 7.0- at smallerscales, expressed in patternsof be- ha quadrats).With increasinggrain size from havior and prey selection(Eckhardt 1979, Rob- the 0.25-haquadrats to the 7.0-haquadrats, much inson and Holmes 1984). Preferences could be- of the fine-scalebetween-quadrat variation in comeapparent with an expandedspatial extent habitat was lost to resolution (Wiens 1989),and that includeda greatervariety of habitattypes the relatively broad-scale habitat variation (Wiens and Rotenberry1981). Patchy disper- among transectsthat represented different suc- sion patternsand correspondinglystrong hab- cessionalstages emerged. Prairie Warblersap- itat relationshipswould likely emergeas the pearedto be respondingto thisbroad-scale hab- extent was increased to include unfavorable itat variationwith patchydispersions emerging habitat. In addition, more consistent indicators at the 3.5-ha and 7.0-ha scales. of habitatquality might be revealedby exam- Although we did not determine habitat as- ining reproductive success(Van Horne 1983, sociations at the 3.5-ha scale of resolution, we Maurer 1986). can gain insight into the possibility of re- The regular dispersionsof all three species sponsesto habitatvariation by Prairie Warblers within transectsreflect the effectsof intraspe- at this grain size by consideringthe resultsof cificterritoriality. This finding was not surpris- the within-transectdispersion analysis and by ing given the high densitiesof these species examining habitat variation within transects. within fairly homogeneoustransects. Regular Prairie Warbler density varied considerably dispersionpatterns also emerged among tran- among transects(Fig. 1). BecausePrairie War- sectsat fine grain sizes.The spatial (between- bierswere dispersedregularly even in transects quadrat)variance in habitatat smallgrain sizes where they were lessabundant (Table 2), they tendsto be relativelylarge (Wiens 1989). There- probably respondedto within-transecthetero- fore, the broad-scalepatterns of habitat varia- geneity, spacing themselves evenly within tion were unresolvedat the smallgrain sizesof preferableareas. Indeed, the transectson which the 0.25-and 0.5-ha quadrats. These small grain Prairie Warblers were least abundant (MASH1 sizes roughly correspondedto the sizes of in- and MASH2) were characterizedby higherand dividual territories and, therefore, there was not moreheterogeneous coverage of trees(Fig. 2). muchvariation in the numberof territorycen- The Prairie Warblers' responseto this within- ters per quadrat.Low valuesof the dispersion transectheterogeneity is supportedby the find- indicesreflect the regulardispersion patterns. ing that the dispersion of Prairie Warblers at All three specieswere regularlydispersed at 274 MORIMOTOAND WASSERMAN [Auk, Vol. 108 the small extent of individual transects,pre- ing spot mappingand transecttechniques. Con- sumably becauseof intraspecific competition. dor 86: 237-241. PrairieWarblers became first randomlyand then CLARK,P. J., & F. E. EVANS. 1954. Distance to nearest patchily dispersed,whereas Rufous-sidedTo- neighboras a measureof spatialrelationships in whees and Common Yellowthroats became ran- populations.Ecology 35: 445-453. COHEH,J. 1960. A coefficientof agreementfor nom- domly dispersed,as the grain size increasedto inal scales.Educ. Psychol.Measur. 20: 37-46. reflect dispersionin relation to broad-scalesuc- COHHER, R. N., J. G. DICKSOH, B. A. LOCKE,& C. A. cessionalpatterns within the southeasternMas- SEGELQUIST.1983. Vegetationcharacteristics im- sachusettspine barrens. These results, in com- portant to commonsongbirds in eastTexas. Wil- bination with the analyses of habitat son Bull. 95: 349-361. associations,imply that thesethree pine barrens Cox, G.W. 1987. Nearest-neighbourrelationships speciesrespond to habitat variation in different of overlapping circlesand the dispersionpattern ways and at different scales,consistent with an of desert shrubs. J. Ecol. 75: 193-199. individualistic view of communities. 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Second-orderneigh- Wasil,the PlymouthCounty Wildlands Trust, the town borhoodanalysis of mappedjoint patterns.Ecol- of Mashpee, and the MassachusettsDivision of For- ogy 68: 473-477. estsand Parks of the Department of Environmental GREIG-SlvtlTH,P. 1961. The use of pattern analysisin Management for granting permission to establish ecologyinvestigations. Rec. Adv. Botany2: 1354- transectson their properties.For help of variouskinds 1358. we thank T. Lloyd-EvansßT. Kurtz, S. Duncan, J. GUTZWlLLER,K. J., & S. H. ANDERSON. 1987. Multi- Kricher, R. Tamarin, R. Formanß G. Adlerß B. Noon, scale associationsbetween cavity-nesting birds O. Jarvinen, D. Phillipsßand especiallyS. Morimoto and featuresof Wyoming streamsidewoodlands. and J. Morimoto. J. Dunning and an anonymousre- Condor 89: 534-548. viewer provided many very valuablecomments and HILL, M.O. 1973. Diversity and evenness:a unifying suggestionswhich greatly improvedthe manuscript. notationand its consequences.Ecology 54: 427• This researchwas in part supportedby the Frank M. 432. Chapman Memorial Fund of the American Museum HOLMES, R. T., T. W. SHERRy,& F. W. STURGES. 1986. of Natural History, the E. AlexanderBergstrom Me- Bird communitydynamics in a temperatedecid- morial Fund of the Association of Field Ornitholo- uousforest: long-term trends at Hubbard Brook. gists,the Societyof SigmaXi, the Thorton W. Burgess Ecol. Monogr. 56: 201-220. Society,and the BostonUniversity GraduateSchool. J•ES, F. C. 1971. Ordinations of habitat relation- shipsamong breeding birds. Wilson Bull. 83: 215- 236. LITra•tORE CITED , & H. H. SHUGARtJR. 1970. A quantitative ADLER,G. H. 1985. Habitat selection and species method of habitat description. Audubon Field interactions:an experimentalanalysis with small Notes 24: 727-736. mammal populations.Oikos 45: 380-390. KERLINGER,P. 1984. Avian communitystructure along AMBUEL,B. A., & S. A. TEMPLE.1983. Area-dependent a successionalgradient in the New JerseyPine changesin the bird communitiesand vegetation Barrens.Rec. New JerseyBirds 9: 71-78. of southernWisconsin forests. Ecology 64: 1057- Klmsl-mw,K.A. 1973. Quantitativeand dynamicplant 1068. ecology.London, William Clowes and Sons. BRUSH,T. 1987. Birds of the central pine barrens: LLOYD-EvANS,T. 1973. Pitch pine-scrub oak forest abundanceand habitatuse. Bull. New JerseyAcad. I-III. Am. Birds. 27: 975-977. Sci. 34: 5-17. 1974. The ornithology and vegetationof a CHRIStMAN,S. P. 1984. Plot mapping: estimating burnt-over pitch pine forestin Plymouth County, densitiesof breeding bird territories by combin- Massachusetts.Mahomet Bird Obs. Res.Rep. 7. April 1991] DispersionPatterns in PineBarrens 275

MAURER,B. A. 1986. Predictinghabitat quality for of spatial configurationsof circles rather than grasslandbirds using density-habitatrelation- pointsßEcology 60: 679-685ß ships.J. Wildl. Manage.50: 556-566. SIMPSON,E. H. 1949. Measurementof diversityßNa- MEAGLER, T. R., & D. S. BURDICK. 1980. The use of ture 163: 688. nearestneighbor frequency analysis in studiesof SOKAI•,R. R., & F. J. ROI-II•F. 1969. Biometry. San association.Ecology 61: 1253-1255. Francisco, California, W. H. Freemanß MORRISON,M.L. 1984. Influence of samplesize on SMITH, K. G. 1977. Distribution of summer birds discriminantfunction analysis of habitatuse by along a forest moisturegradient in an Ozark wa- birds. J. Field Ornithol. 55: 330-335. tershed.Ecology 58: 810-819ß NOL•i, V., JR. 1978. The ecologyand behavior of Trrus, K., J. A. MOSHER,& B. K. WIL•,mMS. 1984. the Prairie Warbler Dendroica discolor. Ornithol. Chance-corrected classification for use in dis- Monogr. 26, Washington, D.C., Am. Ornithol. criminant analysis:ecological applicationsß Am. Union. Midl. Nat. 111: 1-7. NOON,B.R. 1981. The distributionof an avianguild VANDERMEER,J. 1981. Elementary mathematical along a temperate elevational gradient: the im- ecology. New York, John Wiley and Sons. portance and expressionof competition. Ecol. VAN HORNE,B. 1983. Density as a misleading in- Monogr. 51: 105-124. dicator of habitat quality. J. Wildl. Manageß47: NORUSIS,M.J. 1985ßSPSSX advanced statistics guide. 893-901ß New York, McGraw-Hill. WIENS,J.A. 1984. On understandinga non-equilib- O'NEILL, R. V., D. L. DEANGELIS,J. B. WAIDE,& T. F. rium world: myth and reality in community pat- H. ALLEN. 1986. A hierarchical conceptof eco- terns and processesßPp. 439-457 in Ecological systems.New Jersey,Princeton Univ. Press. communities:conceptual issues and the evidence PHILLn'S,D. L., & J. A. MACM^HON. 1981. Compe- (D. R. Strong Jr., D. Simberloff, L. G. Abele, and tition and spacing patterns in desert shrubs. J. A. B. Thistle, Eds.). Princeton, Princeton Univ. Ecol. 69: 97-115. Pressß RICE,J., R. D. OHMART,& B. W. ANDERSON.1981. Bird ß 1989. Spatialscaling in ecologyßFunct. Ecol. communityuse of riparian habitats:the impor- 3: 385-397ß tance of temporal scalein interpreting discrim- -, J. F. ADDICOTr, T. J. C•,SE,& J. DmMOND. 1986. inant analysis.Pp. 186-196 in The usesof mul- Overview: the importanceof spatial and tem- tivariate statistics in studies of wildlife habitat poral scalein ecologicalinvestigationsß Pp. 145- (D. E.Gapen, Ed.). U.S. Dep. Agric. For. Serv. Gen. 153 in Community ecology(J. Diamond and T. J. Tech. Rep. RM-87. Case,Eds). New York, Harper and Row. ß , & . 1983. Habitat selection ß & J. T. ROTENBERRY.1981. Habitat associa- attributesof an avian community:a discriminant tionsand communitystructure of birds in shrub- analysisinvestigation. Ecol. Monogr. 53: 263-290. steppe environments. Ecol. Monogr. 51: 21-41ß ROBINSON, S. K., & R. T. HOLMES. 1984. Effects of --, & B. VAN HORNE. 1987. Habitat oc- plant speciesand foliage structureon the for- cupancy patterns of North American shrub- aging behavior of forest birds. Auk 101:672-684. steppebirds: the effectsof spatialscaleß Oikos 48: SHANNON, C. E., & W. Wr•vER. 1949. The mathe- 132-147ß maticaltheory of communication.Urbana, Univ. WIL•,mMS,B.K. 1981. Descriminantanalysis in wild- Illinois Press. life research:theory and applicationsßPp. 54-71 SHERRY,T. W., & R. T. HOLMES.1985. Dispersion in The uses of multivariate statistics in studies of patternsand habitat responsesof birds in north- wildlife habitat(D. E. Gapen,Ed.). U.S. Dep. Agric. ern hardwoodforests. Pp. 283-309in Habitatse- For. Serv. Gen. Tech. Rep. RM-87. lection in birds (M. L. Cody, Ed.). New York, 1983. Some observations on the use of dis- Academic Press. criminant analysisin ecologyßEcology 64: 1283- SIMBERLO•,D. 1979. Nearest-neighborassessments 1291. 276 MORIMOTOAND WASSERMAN [Auk, Vol. 108

APPENDLXI. Transect name, location, size (ha), num- APPENDLX2. Variablesmeasured to characterizeveg- ber of years sincelast burned, and number of 0.04- etation in the southeasternMassachusetts pine bar- ha vegetationplots sampled for eight strip transects rens. in the southeasternMassachusetts pine barrens. Variable code Description Yearspost- No. of Coverage burn vegetation % cover scrub oak < 1 m Transect Location Size (to 1985) plots SOAK2 % cover scrub oak > 1 m 1 PCWT Plymouth 7.0 15 32 BHUCK % coverblack huckleberry 2 MSSF1 Plymouth 7.0 22 34 BLUEB % coverblueberry 3 MSSF2 Plymouth 7.0 >30 32 SLAUR % cover sheeplaurel 4 MSSF3 Plymouth 7.0 10 32 SFERN % cover sweet fern 5 PLIM Plymouth 7.5 21 33 HERB % cover herbs 60AFB Bourne 7.0 5 32 SMIL % cover commongreenbriar 7 MASH1 Mashpee 9.0 >30 40 PPINE1 % cover pitch pine <8 m 8 MASH2 Mashpee 7.0 >30 32 PPINE2 % cover pitch pine >8 m WPINE % cover white pine BOAK % cover black oak WOAK % cover white oak

Structure

BASAL Basal area of trees >5 cm DBH BASAL3 Basalarea of the 3 largesttrees DEADBAS Basalarea of standingdead trees HITS Total number of contactswith vegetationpole MAXHGT Maximum height of vegetation FALL Number of fallen trees >5 cm DBH LITT Averagelitter depth Heterogeneity FHD Foliageheight diversity FPD Foliageprofile diversity