ANDREW P. ALLEN and RAYMOND J. O’CONNORdrewa/pubs/allen_ap_2000_e62_15.pdf · ANDREW P. ALLEN1;2...

HIERARCHICAL CORRELATES OF BIRD ASSEMBLAGESTRUCTURE ON NORTHEASTERN U.S.A. LAKES

ANDREW P. ALLEN1,2∗ and RAYMOND J. O’CONNOR11 Department of Wildlife Ecology, University of Maine, 5755 Nutting Hall, Orono, ME 04469

U.S.A.;2 Dynamac Corporation, 200 SW 35th Street, Corvallis, OR 97333 U.S.A.∗ Present address and address for correspondence: Department of Biology, University of New

Mexico, 167 Castetter Hall, Albuquerque, NM 87131 U.S.A., e-mail: [email protected]

(Received 17 July 1998; accepted 29 January 1999)

Abstract. We investigated the factors structuring lake shore bird assemblages of the northeasternU.S.A. using data collected from 158 lakes between 1992 and 1994. The assemblage data wereaggregated and standardized to produce assemblage compositions consisting of proportions of indi-viduals employing different foraging, dietary, and migratory strategies. The assemblage data werethen re-expressed using compositional analysis techniques and subjected to regression tree analysisto identify environmental correlates over a range of scales. Regionally, human density in the wa-tershed was the most important predictor for the foraging, dietary, and migratory compositions.A combination of anthropogenic and non-anthropogenic factors likely contributed to these broad-scale associations because land use was largely confounded with climate and geomorphology. Morelocally, associations with lake shore residential-urban development were identified as being importantfor the foraging and dietary compositions, as were associations with lake shore wetlands, but onlycontingent there being little human development present locally and regionally. Assemblages wereassociated with increasing local and regional human development such that: hawking, aerial foraging,and ground gleaning individuals increased relative to hover-and-gleaners, foliage gleaners, and barkgleaners; omnivores increased relative to insectivores; and residents increased relative to neotrop-ical migrants. The observed changes in bird community structure were consistent with declines inforest interior species relative to edge species in response to forest fragmentation and suggest thatanthropogenic factors have played a prominent role in structuring lake shore bird assemblages of thisregion.

Keywords: CART, compositional analysis, forest fragmentation, lake, neotropical migrant, regionalecology, riparian

1. Introduction

Each species relates to its environment in an individualistic manner (Taperet al.,1995), but suites of bird species may show similarity in their responses to forestfragmentation based on common features of their life history (Hansen and Urban,1992). Forest fragmentation involves subdividing contiguous forests into patches,reducing forest patch size, increasing forest patch isolation (Andren, 1994), andincreasing the density and abruptness of habitat transitions (i.e., edges) in thelandscape (Gustafson and Parker, 1992). Forest fragmentation may occur as a res-ult of natural disturbance (e.g., fire, windfall), but its most important and broad-

Environmental Monitoring and Assessment62: 15–37, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

16 A. P. ALLEN AND R. J. O’CONNOR

scale cause is the expansion and intensification of human land use (Burgess andSharpe, 1981). Many neotropical migrants inhabiting deciduous forests of the east-ern U.S.A. have been adversely affected by human-induced forest fragmentation(Robinsonet al., 1995; Flather and Sauer, 1996) because other related aspects oftheir life history (e.g., relatively low reproductive potential) render them particu-larly susceptible to broad-scale forest fragmentation effects such as increased nestpredation and brood parasitism (Whitcombet al., 1981). Besides making the land-scape more accessible to predators and brood parasites, forest fragmentation altersthe relatively availability of foraging substrates (Blake, 1983). Insectivorous birdsare often adversely affected by these changes (Tellaria and Santos, 1995), whereasomnivores and granivores are often unaffected or respond favorably because oftheir ability to exploit forest edges and human-built environments (Whitcombetal., 1981; Ambuel and Temple, 1983; Blake, 1983; Andren, 1992).

The goal of this study was to assess the relative influences of anthropogenic andnon-anthropogenic factors in structuring lake shore bird assemblages of the north-eastern U.S.A. with regard to foraging, dietary, and migratory strategies. To helpdiscern influences on community structure while controlling for bird abundance,the data were aggregated and standardized to produce assemblage compositionsconsisting of relative proportions of individuals employing different life historystrategies. By definition, the components of a composition must sum to unity (e.g.,the dietary composition consisted of insectivore, omnivore, and granivore indi-vidual proportions summing to 1), thereby invalidating many statistical procedures(Aitchison, 1986). Assemblage compositions were transformed into a form amen-able to statistical treatment using compositional analysis techniques (Aitchison,1986) and then subjected to classification and regression tree analysis (CART,Breimanet al., 1984) to identify environmental correlates over a range of scales.

2. Methods

2.1. LAKES ANALYZED

Lake shore bird and habitat data were collected from 158 lakes in New Eng-land, New York, and New Jersey by the US Environmental Protection Agency’s(EPA) Environmental Monitoring and Assessment Program (EMAP) (Whittier andPaulsen, 1992). Twenty-eight, 67, and 63 lakes were surveyed in 1992, 1993, and1994, respectively. Each year’s sample of lakes was similarly and independentlyselected using a regional probability design and so could be used to draw infer-ences on all lakes of the region with known statistical confidence (Larsenet al.,1994). Regional estimates of lake population parameters for different years wereindependent because only one year’s data was analyzed for each lake.

HIERARCHICAL CORRELATES OF BIRD STRUCTURE 17

2.2. FIELD METHODS

Surveys were conducted from a canoe along a transect 10 m from and parallel to thelake shore during the breeding season (i.e., late May through early July) when birdswere most vocal (Bakeret al., 1997). On small lakes, data were collected every200 m along the entire lake perimeter. On lakes with perimeters> 4.8 km, 24 stopswere stratified with respect to the relative occurrence of habitats along the lakeshore. Surveys were conducted between 0.5 h before sunrise and 4 h after sunriseon days with minimal wind and precipitation. At each stop, all individuals seenor heard within a 100 m radius during a 5 min period were identified to species.Another observer simultaneously recorded details of habitats found within the plot.

2.3. COMPOSITIONAL ANALYSIS

Each species was classified in three ways: to a functional group defined by its for-aging technique, to one defined by its dietary preference, and to one defined by itsmigratory status (Appendix). Functional groups with individuals present on fewerthan half the lakes were not analyzed because they largely contributed presence-absence information and so were not amenable to treatment using our proportionalabundance approach. We analyzed six foraging groups (Ehrlichet al., 1988): aerialforagers (capture insects while in prolonged flight), bark gleaners, foliage gleaners,ground gleaners, hawkers (sally from their perches on short flights to capture flyinginsects), and hover-and-gleaners (glean from foliage while hovering); three dietarygroups (Erhlichet al., 1988): insectivores, omnivores, and granivores; and twomigratory groups (Finch, 1991): migrants and residents. All birds surveyed alonglake shores were analyzed, but most were terrestrial passerines (89%).

Total bird abundance varied significantly over the three year period of the study(Kruskal-Wallis Test, P< 0.001). In an attempt to control for temporal changesin abundance, community data were converted to foraging, dietary, and migratorycompositions (e.g., the dietary composition consisted of insectivore, omnivore, andgranivore proportional abundance values that summed to unity). To test for theyear effect on the three assemblage compositions, matrices were first re-expressedusing the log-ratio transformation:yi = ln(xi/xj ); i = 1, . . .,D − 1; j = D;whereD is the number of components in the composition andxi andyi are theraw proportion and transformed value, respectively (Aitchison, 1986). The log-ratio transformation is equivalent to centering the ln(xi) with respect to their mean,so results are independent of the componentxj chosen for the denominator. Nullvalues were replaced with an arbitrary small value (in this case, 0.01) prior totransformation to avoid division by 0, as recommended by Aitchison (1986). Thelog-ratio-transformed matrices consisted of one less column than the matrices fromwhich they were derived but no information was lost owing to the unit-sum con-straint for the compositions. We tested for the year effect using an F-test in analysisof variance (ANOVA) for the log-ratio-transformed migratory composition. Wetested for the year effect using Wilk’s lambda log-likelihood ratio test in multiple


analysis of variance (MANOVA) for the foraging and dietary compositions becausetheir log-ratio-transformed matrices consisted of multiple columns, necessitatingseveral dependent variables. We failed to find significant between-year differencesin relation to foraging technique (P = 0.45), dietary preference (P = 0.44), ormigratory status (P = 0.77). Assemblage compositional data for the three yearperiod could therefore be combined for subsequent analysis without giving furtherconsideration to temporal changes in abundance.

The log-ratio matrix for the migratory composition was univariate owing to thebinary classification scheme used for migratory status and the unit-sum constraintfor the composition. The log-ratio values themselves could therefore serve as aunivariate response for subsequent analysis. The foraging and dietary composi-tions were multivariate because each was comprised of more than two functionalgroups. One approach to analyzing these data would be to analyze the proportionalabundance of each functional group separately after normalizing its values usingan appropriate transformation. However, this ignores the non-independence of theproportions in the composition and any relationships they may have. We thereforechose instead to use log-contrast principal components analysis (PCA) (Aitchison,1986), an approach that served two purposes here: (1) it identified the dominantrelationships among functional groups in the foraging and dietary compositions,and (2) it isolated those relationships on independent axes of variation, therebydecomposing the multivariate data into a series of univariate measures that couldthen be separately analyzed.

To perform this method of PCA, raw proportional data are first transformed us-ing the centered log-ratio transformation:yrc = ln{xrc / (xr1, . . ., xr,D)1/D}, whereD is equal to the number of components in the composition,xrc andyrc are theraw proportion and transformed value, respectively, for rowr and columnc, withthe geometric mean of all components in the composition serving as the commondivisor for each row. As before, null proportions were replaced with values of0.01 prior to transformation to avoid division by 0. A standard PCA algorithmis then applied to the covariance matrix (the use of correlation matrices is invalid,Aebischer personal communication) derived from the centered log-ratio data. Thenumber of PCA axes generated is one fewer than the number of components inthe composition, a consequence of each row summing to 0 for centered log-ratio-transformed matrices. Results of log-contrast PCA are otherwise interpreted in amanner similar to the standard approach except that log-contrasts (so called be-cause logarithms sum to 0) replace linear combinations of variables. We plotted themagnitudes of the eigenvalues in sequence (scree plots; Rencher, 1995) to assesswhich components provided informative descriptions of the variance.

2.4. REGRESSION TREE ANALYSIS

The log-contrast principal component scores (foraging and dietary compositions)and log-ratio-transformed values (migratory composition) were entered as response


variables in CART (Breimanet al., 1984) to identify correlates of assemblage struc-ture. CART was chosen for this analysis because it yields hierarchically-organizedmodels with lower level associations contingent on those specified above (Clarkand Pregibon, 1992), empowering the method to consider the scale- and context-dependence of ecological patterns (O’Connoret al., 1996). CART also facilitatesmapping of the correlations when the data are spatially registered to determine howcorrelations are propagated spatially (e.g., O’Connoret al., 1996).

CART models are fitted by successively dichotomizing the data on the explan-atory variables that minimize the residual sum-of-squares of the response variablesummed across the two resultant groups (Venables and Ripley, 1994). The CARTmodel will provide a perfect fit to the data if splitting continues exhaustively, butthis model will perform poorly when applied to new data because it is over-fitted toa single sample from the statistical population. We took the approach recommendedby Breimanet al. (1984) to address this issue: the model was initially over-fittedand then subsequently reduced in size to yield a statistically valid model. Cross-validation methods were used to determine the appropriate size for each model(Venables and Ripley, 1994).

The explanatory variables analyzed constituted a spatially-nested hierarchy thatcharacterized the landscape, watershed, lake shore, and lake basin (Table I). Vari-able transformation was unnecessary because CART models are invariant to mono-tonic re-expressions of the predictors (Clark and Pregibon, 1992).

2.4.1. Landscape VariablesLandscape compositional measures derived from AVHRR satellite imagery andthe Digital Chart of the World were spatially registered to a 640 km2 grid ofhexagons whose centers were 27 km apart by O’Connoret al. (1996), as was aseasonal climatic index (seasonality, difference between January and July meantemperature) derived from the Historical Climate Database. Values for climatic andlandscape compositional measures were assigned as attributes for lakes present ineach hexagon.

2.4.2. Watershed VariablesThe land use-land cover (LULC) classification of Andersonet al. (1976) was usedto determine the proportions of the watershed in various land classes. The LULCdata classified land differently than the landscape hexagon data and were derivedfrom finer-scale imagery. As an alternative means of assaying human development,human and road densities within each watershed were estimated using 1990 USCensus data and the TIGER file database, respectively (US Bureau of Census,1990). LULC land use, road density, human population density, and point-sourcepollution density were determined for each watershed by EMAP.


TABLE I

Explanatory variables analyzed

Spatial scale Explanatory variable

Regional-landscape proportions of hexagon in land classesa:

woodland-cropland

coniferous forest

mixed forest

water

urban

hexagon climatic indicesa:

seasonality (◦C) (July–Jan. mean temp.)

mean annual precipitation (mm)

proportions of watershed in land classesb:

any type of human development

urban-commercial development

residential development

agricultural development

forest

wetland

indices of human development in watershedc:

road density (km)

population density (humans km−2)

Lake shore proportions of shoreline dominated by habitatsd:

any types of human development

residential-urban development

agricultural development

deciduous forest

mixed forest

coniferous forest

wetland habitat

proportions of shoreline with habitats presentd:

any type of human development

residential-urban development

residential development

wetland

dead-or-dying trees

average # habitats present per stopd

Data sources or collection methods:aO’Connoret al.(1996),bAndersonetal. (1976),cUS Bureau of Census (1990),dBakeret al. (1997).


TABLE I

(continued)

Spatial scale Explanatory variable

Lake basin water chemistry measured:

pH

conductivity (µS cm−1)

total nitrogen (µg L−1)

total phosphorus (µg L−1)

turbidity (NTU)

lake morphology measure:

area (ha)

average depth (m)

shoreline complexity (unitless)

2.4.3. Lake Shore VariablesThe field habitat data were used to derive lake shore habitat measures that charac-terized the presence and dominance of various anthropogenic and non-anthropoge-nic habitats, the presence of dead-or-dying trees, and the average number of hab-itats per stop (habitat categories: deciduous, mixed and coniferous forest, wetland,agriculture, and residential-urban development).

2.4.4. Lake Basin VariablesWe analyzed the following water chemistry measures because they often reflectincreasing trophic status in the lake and increasing human development in thewatershed: pH, conductivity, total nitrogen, total phosphorus, and turbidity. Basinmorphology measures included lake area, lake depth, and shoreline complexity(computed asDL =L/{2√πAo}, whereDL is shoreline complexity,L is the lengthof shoreline, andAo is the area of the lake).

3. Results

3.1. FORAGING COMPOSITION

The first log-contrast principal component accounted for 46% of the variance inthe foraging composition and contrasted the hover-and-gleaners, foliage gleaners,and bark gleaners (loadings of 0.86, 0.30, and 0.25, respectively) with the aerialforagers, hawkers, and ground gleaners (loadings of –0.55, –0.51, and –0.35, re-spectively; Table II). The hover-and-gleaners had the highest absolute loading onthis axis and were therefore most influential. This was not unexpected given that the


TABLE II

Loadings and proportions of the variance accountedfor by log-contrast principal components of the for-aging (a) and dietary (b) compositions. The numbersin parentheses are the variances for each centeredlog-ratio measure

Assemblage composition PC1 PC2

a) Foraging composition

hover-and-gleaners (0.94) 0.86 –0.26

aerial foragers (0.76) –0.55 –0.63

hawkers (0.65) –0.51 0.39

ground gleaners (0.32) –0.35 0.09

foliage gleaners (0.28) 0.30 –0.01

bark gleaners (0.53) 0.25 0.42

% variance accounted for 46 23

b) Dietary composition

granivores (0.46) –0.68 0.03

omnivores (0.43) 0.37 0.54

insectivores (0.42) 0.31 –0.57

% variance accounted for 52 48

hover-and-gleaners had the highest variance of any centered log-ratio-transformedmeasure (0.94) and thus represented the largest portion of the variability in thecomposition. The bark gleaners appeared least influential because they had the low-est absolute loading despite having a variance of intermediate magnitude (0.53).Subsequent principal components were not analyzed because scree plots suggestedthat their variances were uninformative.

The regression tree model predicting scores on the first principal componentof the foraging composition (hereafter referred to as the foraging model) initiallysplit the original pool of 158 lakes on human density in the watershed (cutoff of26.91 humans per km2) (Figure 1). PCA scores were lower for watersheds abovethe human density cutoff than below, meaning that hawkers, aerial foragers, andground gleaners were proportionally more abundant than hover-and-gleaners, barkgleaners, and foliage gleaners on lakes with higher human densities in their water-sheds (Table II). This split separated lakes in southeastern New England and NewJersey from lakes to the north and west. The regional clustering of lakes on thissplit reflected the fact that human density was correlated with the structure of bird

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Figure 1.Regression tree model predicting the first log-contrast principal component of the foraging composition (refer to Table II for variable loadings).The value in each node is the predicted standardized component score, and the value below corresponds to the number of cases. Lakes in each terminalnode are represented as symbols on the map. Locations of some lakes have been altered to minimize overlap. The variance accounted for by each split isproportional to its vertical distance in the diagram. The variables included in this model are: popden (humans per km2 watershed area, accounting for 48%of the variance) and sdwet (proportion of stops dominated by wetland, accounting for 7% of the variance).


assemblages at broad spatial scales. A secondary split among the populous south-eastern lakes revealed further proportional increases of hawkers, aerial foragers,and ground gleaners where human populations exceeded 169.80 per km2 water-shed area. A secondary split was also present for the lakes of the less populatednorth and west, with hawkers, aerial foragers, and ground gleaners proportionallymore abundant where greater than 12% of the shoreline was dominated by wetland.Assemblages were associated with lake shore wetland at a more local scale thanhuman density in the watershed judging from the fact that lakes with wetlandsabove and below the cutoff did not appear spatially cohesive.

3.2. DIETARY COMPOSITION

The dietary composition’s first principal component accounted for 52% of thevariance and contrasted the granivores with both the omnivores and insectivores(loadings of –0.68, 0.37, and 0.31, respectively; Table II). The second principalcomponent contrasted the insectivores with the omnivores (loadings of –0.57 and0.54, respectively) to account for the remainder of the variance (48%). The loadingsclosely reflected the relative influence of the dietary groups on the two axes becausethe magnitudes of the variances were similar for the three centered log-ratio meas-ures (range: 0.42 – 0.46). Both principal components were retained for subsequentanalysis because each accounted for about half the variance, and collectively theyaccounted for all the variation in the dietary composition. Relationships among thethree dietary groups were fully described by two principal components because ofthe unit-sum constraint for the composition.

The model predicting the first principal component of the dietary composi-tion has not been presented because cross-validation suggested that none of theexplanatory variables listed in Table I were useful predictors.

For the model predicting the second principal component of the dietary compos-ition (hereafter referred to as the dietary model), omnivores were more abundantrelative to insectivores in watersheds with more humans (density above 25.47 perkm2), and increased further if residential-urban development was present alongmore than 12% of the lake shore (Figure 2). As with the foraging model, the initialsplit on human density in the watershed for the dietary model created regionallydistinct lake groups, reflecting its broad-scale association with assemblage struc-ture. On lakes situated in more sparsely populated watersheds (< 25.47 humansper km2), omnivores decreased relative to insectivores as human density in the wa-tershed dropped below 2.55 humans per km2, and were further reduced if shorelinewetlands were present at more than 77% of the stops. Lakes grouped by lower-level splits on shoreline wetland and residential-urban development did not appearspatially cohesive, indicating that these associations occurred more locally than forhuman density in the watershed.

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Figure 2.Regression tree model predicting the second log-contrast principal component of the dietary composition (refer to Table II for variable loadings).The value in each node is the predicted standardized component score, and the value below corresponds to the number of cases. Lakes in each terminalnode are represented as symbols on the map. Locations of some lakes have been altered to minimize overlap. The variance accounted for by each split isproportional to its vertical distance in the diagram. The variables included in this model are: popden (humans per km2 watershed area, accounting for 46% ofthe variance), swet (proportion of stops with wetland habitat present, accounting for 5% of the variance), and sres (proportion of stops with residential-urbandevelopment present, accounting for 4% of the variance).


TABLE III

Variance accounted for by the predictors in each model

Response Predictor % variance

accounted for

Foraging PC1 (foraging model)

all explanatory variables 55

humans per km2 in watershed 48

proportion of shore dominated by wetland 7

Dietary PC1

no explanatory variables were useful predictors 0

Dietary PC2 (dietary model)



proportion of shore with wetland present 5

proportion of shore with residential-urban

development present 4

Migratory composition (migratory model)



3.3. MIGRATORY COMPOSITION

The log-ratio-transformed migratory composition was univariate as a result of thebinary classification scheme used for migratory status, permitting the log-ratio-transformed values to serve as the response in CART. The migratory model initiallydichotomized the data on human density in the watershed to account for 32% ofthe variance. This split identified the 13 assemblages located in the most urbanizedwatersheds (> 287.95 humans per km2) as having proportionally fewer migrantsthan assemblages throughout the rest of the region. In less urbanized watersheds(< 287.95 humans per km2), another split was induced on human density in thewatershed (cutoff of 27.44 per km2) to account for an additional 9% of the variance.This split induced a regional dichotomy similar to that induced by the top splits inthe foraging and dietary models, with migrants proportionally more abundant inthe more sparsely settled northern and western portions of the region.

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Figure 3.Regression tree model predicting the migratory composition. The value in each node is the predicted value, and the value below corresponds tothe number of cases. Predicted values have been expressed as migrant proportional abundance here, but log-ratio-transformed values were used to modelthe data. Lakes in each terminal node are represented as symbols on the map. Locations of some lakes have been altered to minimize overlap. The varianceaccounted for by each split is proportional to its vertical distance in the regression tree diagram. The variable popden (humans per km2 watershed area)accounts for 41% of the variance.


4. Discussion

4.1. CARTMODELS

Anthropogenic variables accounted for the most variance in all three CART mod-els (Table III), taking precedence over non-anthropogenic variables pertaining toclimate, landscape and shoreline composition, lake morphology, and lake waterchemistry. The importance of human density in the watershed as a predictor inthe foraging, dietary, and migratory models, along with the clustered nature oflake groups induced by splits on this variable, indicates that human settlement wasclosely associated with assemblage structure at broad spatial scales. While thesefindings are consistent with studies that have shown the avifauna to be affected byregional forest fragmentation (e.g., Robinsonet al., 1995, Flather and Sauer, 1996),broad-scale non-anthropogenic factors closely associated with human density (e.g.,seasonality, rs = 0.74, P< 0.001; landscape coniferous forest, rs = 0.70, P< 0.001)may have also contributed to these associations. For example, neotropical migrantswere proportionally more abundant in watersheds to the north with more severeclimates and lower human densities (Figure 3). Northward increases in the migrantproportion might be expected, even in the absence of human influence, becauseAshmole’s (1963) hypothesis predicts an increased local enhancement of the biotaby migrant species as seasonal climatic variation increases and resources becomemore ephemeral.

All three models included a cutoff value of approximately 26 for the humandensity variable, identifying lake bird assemblages in southeastern New Englandand New Jersey as being distinct from those in the rest of the region (Figures 1–3).This area corresponds closely with the northeastern coastal zone (southeastern NewEngland) and middle Atlantic coastal plain (New Jersey) ecoregions of Omernik(1987). Omernik (1987) noted that land use often reflects the spatial patterningof ecosystems because humans integrate causal agents that dictate the potentialsand capacities of the land (e.g., land surface form, potential natural vegetation,soils). Allan and Johnson (1997) observed that land use and surficial geology oftenshow a close correspondence, making it difficult to determine their independenteffects on the stream biota. The broad-scale associations we observed betweenbird assemblages and human settlement thus probably reflected a combination ofhuman effects on bird assemblages and commonalty in the environmental gradi-ents integrated by humans and birds. Regardless of human development’s directcontribution to these patterns, the broad-scale nature of these associations indicatesthat local community structure was constrained by factors operating over broaderspatial scales (Ricklefs, 1987). Moreover, the split on approximately 26 humansper km2 watershed area in all CART models suggests that birds were similarlyconstrained by this regional dichotomy with respect to the use of foraging, dietary,and migratory strategies.


Other associations identified by CART may relate more directly to anthropo-genic effects. In the migratory model, the 13 assemblages situated in the mostheavily urbanized watersheds were identified as being migrant depauperate as com-pared to the 145 others (Figure 3). Despite the unbalanced sizes of the lake groupsin this dichotomy, the split accounted for 32% of the variance in the migratorycomposition. This split explained considerably more variance than the subsequentsplit yielding the regional dichotomy (9%), arguing that migrants have declinedmarkedly relative to residents as a result of intensive urbanization. Croonquistand Brooks (1993) observed decreases in migrants relative to resident specieswith increasing human disturbance of the riparian zone, and other investigatorshave documented deleterious effects on migrant populations related to land useat broader scales (e.g., Robinsonet al., 1995; Flather and Sauer, 1996). Anthro-pogenic habitat features also served as lower level predictors in the foraging anddietary models (Figures 1 and 2), indicating local effects of lake shore developmenton bird assemblages. Lake shore wetland was also identified as a lower level pre-dictor in both models, but only for watersheds to the north and west with relativelylow human densities. Habitat features not directly related to human developmentthus appeared to decrease in their importance as the presence of humans increasedlocally and regionally. In addition to providing further evidence that humans wereimportant in structuring assemblages of this region, this finding illustrates thatassemblage-habitat associations changed with the regional context.

4.2. LOG-CONTRAST PCA

The first log-contrast principal component of the foraging groups accounted for46% of the variation in this composition and contrasted hover-and-gleaners, foliagegleaners, and bark gleaners with hawkers, aerial foragers, and ground gleaners(Table II). The latter groups showed a significant positive association with localand regional human development (Figure 1). Recall, however, that the relative im-portance of foraging groups on this axis varied, with the hover-and-gleaners beingmost influential and the bark gleaners least so. The hawkers were numerically dom-inated by species such as the Eastern Kingbird, Eastern Phoebe, and Great-crestedFlycatcher (Appendix), which are often associated with human-manipulated set-tings including forest edges, open woodlands, and suburban and agricultural areas(Ehrlich et al., 1988). The three most abundant aerial foragers (Tree Swallow,Chimney Swift, Barn Swallow; Appendix) also exploit human-built habitat fea-tures (Ehrlichet al., 1988). The positive associations of these two groups withhuman development may therefore reflect their affinity for human-built environ-ments. The ground gleaners are a diverse group of fifty species with varying diets,migratory strategies, and degrees of tolerance to human development (Appendix),but the three species with the highest individual counts (Common Grackle, Red-winged Blackbird, Song Sparrow; Appendix) are often found in wetland habitats(Ehrlich et al., 1988). The affinity of these and other ground gleaning species


for wetlands might help to explain their positive association with wetland habitatin watersheds with relatively low levels of human development. Conversely, thehover-and-gleaners, foliage gleaners, and bark gleaners all require trees as foragingsubstrates, so one would expect these species to be absent from wetlands withfew if any trees. The dependence of these foraging groups on forested settingsmight also to help to explain their negative association with land use becausehuman development generally entails forest removal. Findings from other studiessupport the notion that a species’ sensitivity to habitat fragmentation relates toits dependence on trees: Blake (1983) observed that the abundance of foliage andbark gleaning individuals decreased with forest patch size, and Tellaria and Santos(1995) found that species specializing on tree trunks as foraging substrates were thefirst to disappear from small forest fragments. Constraints imposed on the use ofother life history strategies related to foraging technique may have also contributedto the associations observed here because, for example, all 10 hover-and-gleaningspecies were neotropical migrants (Appendix).

Contrasts between granivorous individuals and insectivores and omnivores onthe first log-contrast principal component of the dietary groups were unrelated toany of the habitat features analyzed despite the fact that this axis accounted forthe majority of the variance in the composition. The second log-contrast PCA axiswas, however, successfully modeled by CART, with insectivores decreasing rel-ative to omnivores as human development increased regionally as well as locallyin the more highly developed landscapes (Figure 2). These results are consistentwith previous studies that have shown forest fragmentation to increase omnivoreabundance (Ambuel and Temple, 1983; Blake, 1983; Andren, 1992) and decreaseinsectivore abundance (Whitcombet al., 1981; Blake, 1983).

5. Conclusion

The compositional analysis approach taken in this study permitted the simultan-eous consideration of multiple functional groups to discern influences on overallcommunity structure. The price paid for this perspective was the obscuring ofabsolute abundance patterns. However, Rahel (1990) recognized multiple perspect-ives (i.e., absolute abundance, proportional abundance, and presence-absence) ashaving merit for understanding community dynamics, and Allen and Starr (1982)argued cogently that many perspectives should be considered to disentangle thecomplexity of ecological systems. The strengths of the associations observed herebetween land use and bird assemblage structure may reflect the fact that our as-semblage compositional measures integrated the opposing responses of human-tolerant and intolerant species to anthropogenic factors. CART also proved usefulbecause it allowed us to resolve environmental correlates over a range of scales andto thus explicitly consider the scale- and context-dependence of these ecologicalpatterns.


Our results suggest that local and regional human development has restructuredlake shore bird assemblages of this region such that: hawking, aerial foraging,and ground gleaning individuals have increased relative to hover-and-gleaners,foliage gleaners, and bark gleaners; omnivores have increased relative to insect-ivores; and residents have increased relative to migrants. These associations areconsistent with declines in forest interior specialists and increases in forest edgeand human-tolerant species in response to forest fragmentation (Whitcombet al.,1981). Our results must be interpreted carefully due to their correlative nature, butthe statistical design of this study (Larsenet al., 1994) leads us to believe thatthese generalizations accurately characterize community-habitat associations forthis region’s entire population of lakes and may therefore reflect the extent to whichhuman-induced forest fragmentation has restructured lake shore bird assemblagesof this region.

Acknowledgements

Sandy Bryce, Denis White, Jean Sifneos, Scott Urquhart, William Glanz, AlanWhite, and two anonymous reviewers provided helpful comments on earlier ver-sions of this manuscript. Amanda Moors, Michael Connerton, Norman Famous,James Harding, and Malcolm Jones helped with many aspects of this project.Seventeen field technicians helped collect the bird and habitat data. People in-volved with the EMAP program that made this study possible include: StevenPaulsen, Phil Larsen, Robert Hughes, Alan Herlihy, and Colleen Burch Johnson.The Biodiversity Research Consortium provided the hexagon data. This study wasfunded by the EPA through cooperative agreement CR823806 with the Univer-sity of Maine (RJOC) and interagency agreement DW14937413-01-1 with the USFish and Wildlife Service (subsequently National Biological Service). We acknow-ledge cooperative agreement 14-16-0009-1557 for this support and cooperativeagreement 14-45-0009-1557 for subsequent support through the USGS BiologicalResources Division. Funding for manuscript preparation was provided by EPAcontract 68-C5-0005 with the Dynamac Corporation.


Appendix

Listing of the 165 species analyzed, their life history designations, and the total number of individu-als counted for the 158 lakes. Foraging and dietary group assignments are those of Ehrlichet al.(1988), and migratory assignments are those of Finch (1991)

Common name Scientific name AOU # Foragea Dietb Migrantc Count

Alder Flycatcher Empidonax alnorum 4663 HA IN NE 75

American Bittern Botaurus lentiginosus 1900 SS PI RE 4

American Black Duck Anas rubripes 1330 DA AI RE 38

American Crow Corvus brachyrhynchos 4880 GG OM RE 244

American Goldfinch Carduelis tristis 5290 FG GR RE 412

American Redstart Setophaga ruticilla 6870 HG IN NE 359

American Robin Turdus migratorius 7610 GG IN RE 508

Bald Eagle Haliaeetus leucocephalus 3520 HP PI RE 7

Bank Swallow Riparia riparia 6160 AF IN NE 186

Barn Swallow Hirundo rustica 6130 AF IN NE 223

Bay-breasted Warbler Dendroica castanea 6600 FG IN NE 41

Belted Kingfisher Ceryle alcyon 3900 HD PI RE 49

Black-and-White Warbler Mniotilta varia 6360 BG IN NE 280

Black-backed Woodpecker Picoides arcticus 4000 BG IN RE 2

Black-capped Chickadee Parus atricapillus 7350 FG IN RE 493

Black-crowned Night-heron Nycticorax nycticorax 2020 SS PI RE 1

Black-throated Blue Warbler Dendroica caerulescens 6540 HG IN NE 132

Black-throated Green WarblerDendroica virens 6670 FG IN NE 124

Blackburnian Warbler Dendroica fusca 6620 FG IN NE 223

Blackpoll Warbler Dendroica striata 6610 FG IN NE 1

Blue-gray Gnatcatcher Polioptila caerulea 7510 FG IN NE 4

Blue-winged Warbler Vermivora pinus 6410 FG IN NE 2

Blue Jay Cyanocitta cristata 4770 GG OM RE 288

Bobolink Dolichonyx oryzivorus 4940 GG IN NE 14

Boreal Chickadee Parus hudsonicus 7400 FG IN RE 3

Broad-winged Hawk Buteo platypterus 3430 SW SM NE 6

Brown-headed Cowbird Molothrus ater 4950 GG IN RE 103

Brown Creeper Certhia americana 7260 BG IN RE 62

Brown Thrasher Toxostoma rufum 7050 GG OM RE 6

Canada Goose Branta canadensis 1720 SU FO RE 301

Canada Warbler Wilsonia canadensis 6860 HG IN NE 87

Cape May Warbler Dendroica tigrina 6500 FG IN NE 6

Carolina Chickadee Parus carolinensis 7360 FG IN RE 28

aForaging groups: AF, aerial forager; AP, aerial patroller; BG, bark gleaner; DA, dabbler; FG, fo-liage gleaner; GG, ground gleaner; HA, hawker; HD, high diver; HG, hover-and-gleaner; HI, highpatroller; LP, low patroller; PR, prober; SD, surface diver; SS, stalk-and-striker; SU, surface dipper;SW, swooper.bDietary groups: AI aquatic invertivore; BI, avian predator; CA, carrion feeder; FO, folivore; FR,frugivore; GR, granivore; IN, insectivore; NE, nectarivore; OM, omnivore; PI, piscivore; SM, smallmammal predator.cMigratory groups: NE, neotropical migrant; RE, resident.


Appendix

(continued)


Carolina Wren Thryothorus ludovicianus 7180 GG IN RE 5

Cedar Waxwing Bombycilla cedrorum 6190 FG FR RE 959

Cerulean Warbler Dendroica cerulea 6580 FG IN NE 3

Chestnut-sided Warbler Dendroica pensylvanica 6590 FG IN NE 104

Chimney Swift Chaetura pelagica 4230 AF IN NE 234

Chipping Sparrow Spizella passerina 5600 GG IN NE 142

Cliff Swallow Hirundo pyrrhonota 6120 AF IN NE 32

Common Goldeneye Bucephala clangula 1510 SD AI RE 11

Common Grackle Quiscalus quiscula 5110 GG OM RE 1098

Common Loon Gavia immer 70 SD PI RE 57

Common Merganser Mergus merganser 1290 SD PI RE 75

Common Nighthawk Chordeiles minor 4200 AF IN NE 14

Common Raven Corvus corax 4860 GG OM RE 26

Common Snipe Gallinago gallinago 2300 PR IN RE 17

Common Tern Sterna hirundo 700 HD PI RE 10

Common Yellowthroat Geothlypis trichas 6810 FG IN NE 615

Cooper’s Hawk Accipiter cooperii 3330 AP BI RE 2

Dark-eyed Junco Junco hyemalis 5670 GG GR RE 107

Double-crested CormorantPhalacrocorax auritus 1200 SD PI RE 28

Downy Woodpecker Picoides pubescens 3940 BG IN RE 45

Eastern Bluebird Sialia sialis 7660 HA IN RE 1

Eastern Kingbird Tyrannus tyrannus 4440 HA IN NE 324

Eastern Phoebe Sayornis phoebe 4560 HA IN RE 192

Eastern Wood-Peewee Contopus virens 4610 HA IN NE 95

European Starling Sturnus vulgaris 4930 GG IN RE 222

Evening Grosbeak Coccothraustes vespertinus5140 GG GR RE 122

Field Sparrow Spizella pusilla 5630 GG IN RE 3

Fish Crow Corvus ossifragus 4900 GG OM RE 2

Golden-crowned Kinglet Regulus satrapa 7480 FG IN RE 142

Gray Catbird Dumetella carolinensis 7040 GG IN NE 371

Great Blue Heron Ardea herodias 1940 SS PI RE 44

Great Crested Flycatcher Myiarchus crinitus 4520 HA IN NE 129

Great Egret Casmerodius albus 1960 SS PI RE 4

Greater Yellowlegs Tringa melanoleuca 2540 PR PI RE 5

Green-backed Heron Butorides striatus 2010 SS PI RE 9

Hairy Woodpecker Picoides villosus 3930 BG IN RE 37

Hermit Thrush Catharus guttatus 7590 GG IN RE 98

Herring Gull Larus argentatus 510 GG OM RE 71

Hooded Merganser Lophodytes cucullatus 1310 SD PI RE 19

Hooded Warbler Wilsonia citrina 6840 FG IN NE 2

House Finch Carpodacus mexicanus 5190 GG GR RE 82

House Sparrow Passer domesticus 6882 GG GR RE 145

House Wren Troglodytes aedon 7210 GG IN NE 28


Appendix

(continued)


Indigo Bunting Passerina cyanea 5980 FG IN NE 6

Killdeer Charadrius vociferus 2730 GG IN RE 13

Laughing Gull Larus atricilla 580 GG AI RE 39

Least Flycatcher Empidonax minimus 4670 GG IN NE 247

Lincoln’s Sparrow Melospiza lincolnii 5830 SS PI NE 8

Louisiana Waterthrush Seiurus motacilla 6760 GG AI NE 4

Magnolia Warbler Dendroica magnolia 6570 HG IN NE 225

Mallard Anas platyrhynchos 1320 DA GR RE 407

Marsh Wren Cistothorus palustris 7250 GG IN RE 3

Mourning Dove Zenaida macroura 3160 GG GR RE 103

Mourning Warbler Oporornis philadelphia 6790 FG IN NE 8

Mute Swan Cygnus olor 1782 SU GR RE 6

Nashville Warbler Vermivora ruficapilla 6450 FG IN NE 104

Northern Bobwhite Colinus virginianus 2890 GG GR RE 1

Northern Cardinal Cardinalis cardinalis 5930 GG IN RE 55

Northern Flicker Colaptes auratus 4120 GG IN RE 101

Northern Harrier Circus cyaneus 3310 LP SM RE 2

Northern Mockingbird Mimus polyglottos 7030 GG IN RE 16

Northern Oriole Icterus galbula 5070 FG IN NE 188

Northern Parula Parula americana 6480 FG IN NE 215

Northern Rough-winged Swallow Stelidopteryx serripennis 6170 AF IN NE 8

Northern Waterthrush Seiurus noveboracensis 6750 GG AI NE 114

Olive-sided Flycatcher Contopus borealis 4590 HA IN NE 14

Orchard Oriole Icterus spurius 5060 FG IN NE 5

Osprey Pandion haliaetus 3640 HD PI RE 20

Ovenbird Seiurus aurocapillus 6740 GG IN NE 406

Palm Warbler Dendroica palmarum 6720 FG IN NE 4

Philadelphia Vireo Vireo Philadelphicus 6260 HG IN NE 2

Pied-billed Grebe Podilymbus podiceps 60 SD AI RE 1

Pileated Woodpecker Dryocopus pileatus 4050 BG IN RE 11

Pine Siskin Carduelis pinus 5330 FG GR RE 15

Pine Warbler Dendroica pinus 6710 BG IN RE 131

Prairie Warbler Dendroica discolor 6730 FG IN NE 16

Prothonotary Warbler Protonotaria citrea 6370 BG IN NE 3

Purple Finch Carpodacus purpureus 5170 GG GR RE 69

Purple Martin Progne subis 6110 AF IN NE 10

Red-bellied Woodpecker Melanerpes carolinus 4090 BG IN RE 11

Red-breasted Merganser Mergus serrator 1300 SD PI RE 8

Red-breasted Nuthatch Sitta canadensis 7280 BG IN RE 145

Red-eyed Vireo Vireo olivaceus 6240 HG IN NE 721

Red-headed Woodpecker Melanerpes erythrocephalus4060 HA OM RE 2

Red-shouldered Hawk Buteo lineatus 3390 LP SM RE 2

Red-tailed Hawk Buteo jamaicensis 3370 HI SM RE 5


Appendix

(continued)


Red-winged Blackbird Agelaius phoeniceus 4980 GG IN RE 920

Ring-billed Gull Larus delawarensis 540 GG OM RE 39

Ring-necked Duck Aythya collaris 1500 SD GR RE 3

Rock Dove Columba livia 3131 GG GR RE 32

Rose-breasted Grosbeak Pheucticus ludovicianus 5950 FG IN NE 27

Ruby-crowned Kinglet Regulus calendula 7490 FG IN RE 35

Ruby-throated Hummingbird Archilochus colubris 4280 HG NE NE 5

Ruffed Grouse Bonasa umbellus 3000 FG OM RE 2

Rufous-sided Towhee Pipilo erythrophthalmus 5870 GG IN RE 54

Rusty Blackbird Euphagus carolinus 5090 GG IN RE 1

Savannah Sparrow Passerculus sandwichensis5420 GG IN RE 10

Scarlet Tanager Piranga olivacea 6080 HG IN NE 69

Sharp-shinned Hawk Accipiter striatus 3320 AP BI RE 1

Solitary Vireo Vireo solitarius 6290 FG IN NE 224

Song Sparrow Melospiza melodia 5810 GG IN RE 715

Sora Porzana carolina 2140 GG GR RE 1

Spotted Sandpiper Actitis macularia 2630 GG IN RE 75

Summer Tanager Piranga rubra 6100 FG IN NE 2

Swainson’s Thrush Catharus ustulatus 7580 FG IN NE 91

Swamp Sparrow Melospiza georgiana 5840 GG IN RE 264

Tree Swallow Tachycineta bicolor 6140 AF IN RE 875

Tufted Titmouse Parus bicolor 7310 FG IN RE 67

Turkey Vulture Cathartes aura 3250 HP CA RE 11

Veery Catharus fuscescens 7560 GG IN NE 198

Vesper Sparrow Pooecetes gramineus 5400 GG IN RE 2

Virginia Rail Rallus limicola 2120 PR IN RE 5

Warbling Vireo Vireo gilvus 6270 HG IN NE 75

Whip-poor-will Caprimulgus vociferus 4170 AF IN NE 1

White-breasted Nuthatch Sitta carolinensis 7270 BG IN RE 51

White-eyed Vireo Vireo griseus 6310 FG IN NE 1

White-throated Sparrow Zonotrichia albicollis 5580 GG IN RE 93

White-winged Crossbill Loxia leucoptera 5220 FG GR RE 21

Wild Turkey Meleagris gallopavo 3100 GG OM RE 1

Willow Flycatcher Empidonax traillii 4664 HA IN NE 16

Wilson’s Warbler Wilsonia pusilla 6850 FG IN NE 13

Winter Wren Troglodytes troglodytes 7220 GG IN RE 193

Wood Duck Aix sponsa 1440 DA AI RE 21

Wood Thrush Hylocichla mustelina 7550 GG IN NE 68

Worm-eating Warbler Helmitheros vermivorus 6390 FG IN NE 1

Yellow-bellied Flycatcher Empidonax flaviventris 4630 HA IN NE 41

Yellow-bellied Sapsucker Sphyrapicus varius 4020 BG IN RE 85

Yellow-billed Cuckoo Coccyzus americanus 3870 FG IN NE 10


Appendix I

(continued)


Yellow-rumped Warbler Dendroica coronata 6550 FG IN RE 516

Yellow-throated Vireo Vireo flavifrons 6280 FG IN NE 4

Yellow-throated Warbler Dendroica dominica 6630 BG IN NE 3

Yellow Warbler Dendroica petechia 6520 FG IN NE 378

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