The influence of research scale on bald eagle habitat selection along thelower Hudson River, New York (USA)

Craig M. Thompson1,* and Kevin McGarigal2

1Department of Fisheries and Wildlife, Utah State University, Logan, UT 84322, USA; 2Department ofNatural Resource Conservation, University of Massachusetts, Amherst, MA 01003, USA; *Author forcorrespondence (e-mail: cthomp533@aol.com)

Received 16 August 2001; accepted in revised form 16 September 2002

Key words: Extent, Grain, Habitat selection, Haliaeetus leucocephalus, Multi-scale, Scale, Threshold

Abstract

As the concepts of landscape ecology have been incorporated into other disciplines, the influence of spatial pat-terns on animal abundance and distribution has attracted considerable attention. However, there remains a sig-nificant gap in the application of landscape ecology theories and techniques to wildlife research. By combininglandscape ecology techniques with traditional wildlife habitat analysis methods, we defined an ’organism-cen-tered perspective’ for breeding bald eagles (Haliaeetus leucocephalus) along the Hudson River, New York, USA.We intensively monitored four pairs of breeding eagles during the 1999 and 2000 breeding seasons, and col-lected detailed information on perch and forage locations. Our analysis focused on three critical habitat elements:available perch trees, access to foraging areas, and freedom from human disturbance. We hypothesized that eaglehabitat selection relative to each of these elements would vary with the spatial scale of analysis, and that thesescaling relationships would vary among habitat elements. We investigated two elements of spatial scale: grainand local extent. Grain was defined as the minimum mapping unit; local extent was defined by the size of ananalysis window placed around each focal point. For each habitat element, we quantified habitat use over a rangeof spatial scales. Eagles displayed scale-dependent patterns of habitat use in relation to all habitat features, in-cluding multi-scale and threshold-like patterns. This information supports the existence of scale-dependant rela-tionships in wildlife habitat use and allowed for a more accurate and biologically relevant evaluation of HudsonRiver breeding eagle habitat.

Introduction

Ecologists have long recognized the importance ofscale in ecological research. Wiens (1976) referred tothe ’fabric’ of spatial scales, indicating the complexand multi-dimensional relationships inherent in scale-sensitive ecological research. The emergence of land-scape ecology (Forman and Godron 1986; Urban etal. 1987; Turner 1989; Turner et al. 2001) and theconcurrent development of scale and hierarchy theory(Allen and Starr 1982; Wiens 1989) have led to in-creasing concern over issues of scale in ecologicalresearch. In a landscape ecological context, ’scale’generally refers to the resolution, defined in terms of’grain’ and ’extent’, at which the landscape is por-

trayed or perceived by the organism or process underconsideration (Kolasa and Rollo 1991). Grain is theminimum resolution of the data–defined as the cellsize in raster lattice data, the quadrat size in field data,and the minimum mapping unit (polygon) in vector-based data. Extent is the scope or domain of the data–typically defined as the size of the study area or land-scape. In the context of wildlife studies, grain andextent may be defined as the finest and coarsest reso-lution, respectively, of heterogeneity or patch struc-ture to which an organism responds (Kotliar andWiens 1990). Grain and extent set the lower and up-per limits of resolution in the data; that is, we cannotdetect patterns at finer or coarser scales than the grainand extent of the data, respectively, and any infer-

569Landscape Ecology 17: 569–586, 2002.© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

ences about scale-dependency in a system are con-strained by the grain and extent of investigation. Con-sequently, the scale of an investigation influences thelikelihood of detecting resource patches and under-standing their distribution across a landscape. Under-standing the role of scale in wildlife habitat selection,how ecological patterns and processes vary with spa-tial scale, and how the scale of investigation influ-ences both analysis and interpretation of results is animportant component of sound wildlife research(Wiens et al. 1987; Morris 1987; Schulz and Joyce1992; Levine 1992).

The influence of landscape patterns on wildlife andhow these relationships vary with scale has attractedconsiderable attention (e.g., Hall and Mannan (1999)and Peery et al. (1999)). However, there remains asignificant gap in the application of landscape ecol-ogy theories and scaling techniques to wildlife ecol-ogy research (Otis 1997). Most wildlife habitat re-search, conducted either at a single spatial scale or atdiscrete ’micro-’ and ’macro-’ scales, risks missingimportant patterns that are readily apparent at otherscales (Wiens 1989; Baker et al. 1995), or drawingincorrect conclusions regarding habitat use (Orrock etal. 2000). Further, recognition that those patterns rel-evant to target organisms do not necessarily corre-spond to those observed by humans has led to thesearch for ’organism-centered perspectives’, or an at-tempt to allow the study animal to define the scale ofresearch (Wiens (1976) and Turner and Gardner(1991):9). In this regard, Turner and Gardner (1991)emphasized the difference between ’absolute’ and’relative’ scales, where relative scale is defined in afunctionally relevant manner; that is, in a biologicallyappropriate manner relative to a focal organism orecological process.

We investigated bald eagle habitat relationshipsalong the Hudson River Corridor (HRC) in New Yorkin an attempt to integrate the concepts of scale-sensi-tive research into wildlife ecology and management.Several factors make the HRC bald eagle populationa suitable case study for a multi-scale ecological anal-ysis. First, the HRC population of breeding eagles isextremely small, yet it is located in an area of abun-dant resources and appears to be growing steadily(Thompson et al. 2000). Second, eagle habitat re-quirements have been extensively studied and arewell defined. This avoids the problems associatedwith arbitrary definitions of habitat patches or avail-ability (McClean et al. 1998; Turner et al. 2001) andprovides an adequate base of information for inter-

preting scale-dependant relationships. Third, giventhe nationwide increase in eagle populations (Stal-master 1987) as well as recreational and developmentpressure on eagle habitat (e.g., McGarigal et al.(1991) and Stalmaster and Kaiser (1998)), any infor-mation more accurately defining eagle-habitat rela-tionships is crucial for effective management.

We hypothesized that eagle habitat selection wouldvary with spatial scale; that is, that our ability to de-tect selection for certain habitat conditions would de-pend on the scale of the analysis. We further hypoth-esized that these scaling relationships would notnecessarily be consistent among different habitatcomponents. To test these hypotheses, we investi-gated the two major elements of spatial scale: grainand extent (Figure 1). Here, grain was defined as theminimum mapping unit, and extent was defined bythe size of a local analysis window around an animalat a fixed point (e.g., perch site or foraging location).Note, we distinguished between ’local extent’, de-fined by the local neighborhood around a fixed point,and ’global extent’, defined by the animal’s homerange. Local extent was further characterized by twocomponents: local composition (i.e., the habitat com-position of the analysis window) and local configura-tion (i.e., the spatial configuration of habitat patcheswithin the analysis window). We systematically andindependently varied the scale of analysis for severalcritical habitat components and compared our abilityto detect habitat selection among scales. By varyingthe scale continuously over a wide range, we lookedfor patterns in habitat selection indicating criticalthresholds or ’domains of scale’ (Wiens 1989). Habi-tat components were analyzed independently to testwhether eagles select resources at similar scales andto explore how this selection varied across spatialscales (multi-scale responses). Such a hierarchical ap-proach can allow researchers to identify biologicallymeaningful scales and better define how patterns varyacross scales (Wiens 1989; Kotliar and Wiens 1990).

Methods

Study area

The study area comprised approximately 30 miles ofthe upper Hudson River between Stuyvesant andKingston, New York, USA (Figure 2). Bald eaglesbegan returning to the HRC by the mid-1980s afterdisappearing in the early 1900s; at the time the study

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was conducted there were only four known nestingpairs. The New York State Department of Environ-mental Conservation (NYSDEC) is actively involvedin identifying and protecting critical eagle habitat inthis region.

In the study area, the Hudson River is a freshwa-ter tidal system. Tidal flows range from 10 to 100times the total freshwater inflow, resulting in a tiderange of 1–2 meters. Numerous islands, peninsulas,dikes, and tributary inflows, combined with the strongtidal influence, create a complex system of mudflats,tidal wetlands, and side channels. This system of tidalwetlands and tributaries provides spawning groundsfor anadromous fish species, including striped bass(Morone saxatilis), American shad (Alosa sapidissi-ma), and blueback herring (Alosa aestivalis) (USFWS1996).

Human activity along the HRC includes shorelinedevelopment and both commercial and recreationalactivity. During the summer months, recreational

fishing and boating are popular, as well as campingand hiking. Since 1910, the main channel has beenrepeatedly dredged to maintain standard shippingdepths, and commercial shipping continues year-round. The construction of railroad dikes along theeastern shore has significantly altered the shorelinethrough both straightening and creating impoundmentwetlands (Young and Squires 1993). The shoreline isa mix of urban, industrial, low-density residential, anddeciduous forest dominated by Eastern cottonwood(Populus deltoides) and oaks (Quercus spp.), as wellas infrequent conifer stands consisting of Easternwhite pine (Pinus strobus) and Eastern hemlock(Tsuga canadensis).

Data collection

We intensively monitored four pairs of breeding ea-gles during the 1999 and 2000 breeding seasons(April through August). Of the eight breeding adults

Figure 1. Conceptual model of elements of landscape scale influencing an organism’s selection of habitat. Three primary components ofspatial scale: landscape grain, composition, and configuration are modified by steadily increasing the scale of research. With respect to land-scape grain, details are lost as the scale is increased due to the disappearance of small patches. In contrast, detail is increased with theincreasing scale of composition and configuration due to the inclusion of additional area.

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monitored, five were captured using a floating fishmethod (Cain and Hodges 1989; Jackman et al. 1993)and equipped with a 65-g backpack transmitter (Com-munication Specialists, Orange CA) (150.0–151.9MHz). We monitored eagles by boat at > 400 m toreduce the possibility of impacting their behavior. Ob-servations were conducted during continuous 8- to10-hour bouts, one day/week for each pair. We usedtelemetry to maintain visual contact with eagles, butwe only recorded visual observations to maintain ac-curate and consistent data collection. While telemetrysimplified locating eagles and increased the observa-tion distance, we were able to visually monitor eagleswith or without transmitters due to their tendency tohabitually use high-profile perches (Stalmaster 1987;Garrett et al. 1993).

Throughout the breeding season, observations fo-cused on the eagle not attending the nest. Perch loca-tions and foraging attempts, as well as any temporaryhuman activity observed (e.g., fishing boats, shoreline

pedestrian traffic, campsites, etc.), were plotted on1:7,500 aerial photographs (TVGA Engineering, Ith-aca NY) and transferred to an ArcView GIS database(ESRI, Redlands, CA). Individual trees or other perchlocations were easily discernible on the aerial photosand shoreline habitat use was therefore plotted towithin five meters. We estimated that foraging loca-tions over open water were accurate to within 20meters.

While visual observations collected during inten-sive observation bouts are highly autocorrelated andtherefore not independent, with sufficient observationthis method allows for an accurate depiction of pro-portional habitat use in a limited amount of time (Ae-bischer et al. 1993; Otis and White 1999). In addition,eagles’ extreme mobility and our study design reduceconcerns about spatially dependent observations.

Figure 2. Study area location along the Hudson River Corridor, New York.

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Habitat components

Bald eagles are probably the most intensively studiedNorth American raptor, and extensive information ex-ists on their habitat relationships. Past research hasidentified three critical habitat requirements forbreeding eagles: (1) access to foraging areas, (2)perch and nest tree availability, and (3) freedom fromhuman disturbance (Gerrard et al. 1975; McEwan andHirth 1979; Andrew and Mosher 1982; Fraser et al.1985; Watson and Anthony (1986, 1986); Anthonyand Isaacs 1989; Livingston et al. 1990; Buehler etal. 1991; Hardesty and Collpoy 1991; Hunt et al.1992; Bowerman 1993). Consequently, we focusedour investigation of habitat use patterns on these threecomponents.

Access to foraging areasTraditionally, bald eagle foraging is associated withshallow areas such as tidal mudflats (McGarigal et al.1991), shallow rapids (Hunt et al. 1992), or near shore(Grubb 1995). Thus, water depth was used to definepotential foraging areas along the HRC. Informationon water depth was obtained using a NOAA sonarraster data set at a 30-m resolution and 1-m categori-zation beginning at 1-m above mean low tide (ex-posed tidal mudflat) (NOAA Special Projects Divi-sion, Silver Spring MD, 1998). For our analysis, wereclassified these data into four depth classes empha-sizing shallow areas (Table 1).

Available perch/nest treesEagles typically perch in dominant trees or snags thatprovide panoramic views and open flight paths (Stal-

Table 1. Habitat classes associated with each of the four critical habitat components used to assess habitat selection for breeding bald eaglesalong the Hudson River Corridor, New York.

Habitat Component Class Description

Access to foraging areas (water depth):

0–1 m above mean low tide Areas exposed at low tide, primarily tidal mudflats

0–1 m below mean low tide Areas 0–1 m deep at low tide

1–3 m below mean low tide Areas 1–3 m deep at low tide, typically aquatic vegetation beds

> 3 m below mean low tide Deep water areas

Perch tree availability (canopy structure):

Early-mid seral, solid < 5 m tree canopy width, no visible vertical variation or canopy gaps

Early-mid seral, irregular < 5 m tree canopy width, visible vertical variation or canopy gaps

Late seral, solid > 5 m tree canopy width, no visible vertical variation or canopy gaps

Late seral, irregular > 5 m tree canopy width, visible vertical variation or canopy gaps

Disturbance (transitory human activity, descriptions are generalized):

None (0.0) No recorded activity within 500 m of a particular site

Low (0.001–0.2) Few activities recorded 300–500 m from a particular site

Low-moderate (0.021–0.4) Few activities 100–300 m from a particular site

Moderate (0.041–0.6) Periodic activity 100–300 m from a particular site

Moderate-high (0.061–0.8) Periodic activity within 100 m from a particular site

High (0.081–1.0) Intensive activity within 100 m of a particular site

Disturbance (shoreline development): Weight*

Undeveloped No visible development 0.0

Agricultural Agricultural fields and pastures 0.2

Railroad Shoreline railroad tracks 0.2

Road Residential streets, paved and unpaved 0.4

Residential Less than 12 buildings per hectare 0.4

Industrial Industrial buildings and parking lots 0.6

Commercial 12 or more buildings per hectare 0.8

Marina Areas associated with high boating activity 1.0

* Weighting values correspond to the degree of human activity associated with each development type, or the relative disturbance to eagles.

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master 1987). Therefore, the availability of suitableperch trees was represented by canopy structure, sim-ilar to the method used by Peery et al. (1999) to de-lineate Mexican spotted owl habitat. We defined can-opy structure using a combination of seral stage(early-, mid-, and late-seral estimated by tree canopywidth), horizontal canopy variation (presence/absenceof gaps), vertical canopy variation (present/absentbased on shadows), and dominant species (conifer vs.hardwood). We digitized canopy structure polygonson low-level infrared aerial photographs (TVGA En-gineering, Ithaca NY, 1998) using ArcInfo software(ESRI, Redlands, CA, USA). Color infrared imageswere used due to the greater visibility of structuralvariation and intermixed species. Forest patches weredigitized down to individual tree canopies with <10-m error. While eagles are known to perch on openareas such as gravel bars or tidal flats (Stalmaster1987), the canopy structure coverage was restricted toforested areas to more accurately represent perch treeavailability. In order to meet statistical assumptions,canopy structure was collapsed into four categoriesfor analysis (Table 1).

DisturbanceWe examined the influences of two types of humandisturbance: (1) transitory human activities, such asfishing or camping (hereafter referred to as humanactivity), and (2) shoreline development. We collectedinformation on transitory human activities opportu-nistically during eagle observation bouts. These pointlocations were transferred from aerial photographs toa GIS database. While we did not conduct systematicsurveys of human activities, this method allowed forthe identification of activity concentrations, such asfavorite fishing spots, hiking trails or campsites. AGIS coverage of shoreline development was digitizedusing 1:12,000 true-color aerial photographs (NYS-DEC 1997) and ArcInfo software.

The influence of human activity on wildlife radi-ates well beyond the point or patch source location;therefore, merely mapping sources of disturbance isinappropriate. For example, eagles are often disturbedby activities over 500 meters away (Grubb and King1991; Stalmaster and Kaiser 1998) and may habitu-ally avoid areas frequented by humans (McGarigal etal. 1991). This concern is generally addressed bycomparing the number of disturbances or develop-ments in a certain geographic area with eagles’ use ofthat area (Buehler et al. 1991; Brown and Stevens1997; Wood 1999). However, this method does not

account for the differences in the magnitude of hu-man disturbance as the distance between human ac-tivity and eagle decreases.

To account for this radiating influence, we createda landscape-level disturbance index representing thenumber and proximity of surrounding disturbancesfor both human activities and development usingArcInfo GIS. Specifically, disturbance indices werecreated from an initial GIS coverage of disturbancesources–a point coverage for transitory activities anda polygon coverage for development–taken from rec-tified aerial photos. A fine resolution (5-m cell) gridwas overlayed onto the landscape. For each cell in thegrid, all potential disturbances (either transitory activ-ities or development) within 500 m were selected. Adegrading logistic function,

Y � 100 � �A/�1 � exp� � �X � B�/C���

was used to weight each selected disturbance basedon the distance from the focal cell. ’A’ refers to they-intercept; ’B’ refers to the inflection point, equal tothe buffer size divided by two; ’C’ refers to a scalingfactor (buffer size / 8); and ’X’ refers to the distancefrom the focal cell to the observed disturbance.

For human activity, these weighted values weresummed to generate an index value for each cell. Thisvalue can best be thought of as a score, for each pointon the landscape, representing both the number andproximity of surrounding activities. The final disturb-ance index was re-scaled to range from zero to oneby dividing the index by the maximum index valueobserved.

For shoreline development, the influence of eachstructure was weighted both by the above logisticfunction and by the corresponding type of develop-ment (Table 1). This accounted for the variable dis-turbance impacts of different development types (e.g.,marina vs. house). These doubly weighted valueswere then summed to create the final disturbance in-dex for each cell. Buildings were treated as polygons,with each structure being counted only once. Again,this value can best be though of as a two-dimensionallandscape gradient representing the number, type andproximity of surrounding developments. For both hu-man activity and development, mapping these indexvalues created a landscape gradient representing theintensity of disturbance at any point on the landscape,and addressed the cumulative effects of multiple dis-turbances in the same area. Patches were created by

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categorizing index values into six classes of disturb-ance intensity (Table 1).

Data analysis

We analyzed eagle habitat selection associated witheach of the above habitat components over a widerange of spatial scales relative to both grain and localextent (i.e., local composition and configuration). Lo-cations for the male and female of each pair werepooled due to the tendency of eagle pairs to use simi-lar areas (Stalmaster 1987). We defined each pair’sbreeding season home range as the 90% kernel poly-gon estimated using ArcView. The home range wasused to delineate the limits of used and available hab-itat and remained constant through all analyses.

We evaluated habitat selection by comparing habi-tat use versus availability (Neu et al. 1974). One re-current problem in wildlife habitat use vs. availabilityanalyses is the subjective definition of ’available’ hab-itat (Thomas and Taylor 1990; Aebischer et al. 1993;McClean et al. 1998). Eagles along the HRC areclosely tied to the river shoreline, so available perch-ing habitat was defined as a shoreline (i.e., terrestrial)buffer strip that included 95% of observed perch lo-cations (Garrett et al. 1993). This shoreline bufferstrip served as the ’available’ habitat area for assess-ing the ’available perch trees’ and ’freedom from hu-man disturbance’ habitat components for each pair.’Used’ habitat for these same habitat components wasbased on eagle perch locations within this shorelinebuffer strip. Similarly, because HRC eagles forage al-most exclusively in aquatic habitat during the breed-ing season (Thompson et al. 2000), all aquatic areaswithin their home range were considered ’available’foraging habitat for assessing ’access to foraging ar-eas’. ’Used’ foraging habitat was based on foraginglocations.

Patches were considered used when a perch or for-age location fell within its boundaries, and the avail-ability of habitat was based on the proportions of eachpatch type within the eagle’s home range. Perch siteswere weighted by the number of times an eagle re-turned to a particular perch to emphasize high-use ar-eas. Due to the large number of analyses conducted,data were pooled among pairs. While this risks cam-ouflaging divergent behavior by individual eagles orbreeding pairs, eagle behavior has been well docu-mented; our primary objective was to documentscale-dependant habitat selection at the populationlevel. In addition, an analysis of the distribution of

habitat patch types within breeding territories re-vealed no major differences in distribution.

GrainGrain was represented by the minimum mapping unit(MMU) and was systematically varied by graduallyincreasing the MMU from 0.01 ha to 25 ha. This wasdone in a vector-based GIS format to maintain edge-related information and data accuracy. At each MMU,a chi-square test of independence was conducted tocompare used vs. available habitat (Neu et al. 1974;Litvaitis et al. 1996). This approach has proven reli-able in both simulation (Alldredge and Ratti 1986)and field trials (McClean et al. 1998). For ease ofcomparison, the total chi-square values for each land-scape feature were converted to Cramer’s coefficientof association, and were used to assess the degree ofselection at each spatial scale. Selection among habi-tat types was evaluated using the individual confi-dence intervals for the partial chi-square statistics. ABonferroni adjustment of the critical value was usedto compensate for the number of comparisons (Byerset al. 1984). The grain at which the eagles showed thehighest degree of selection for each habitat feature(strongest correlation) was used as the resolution atwhich to map features for the local extent analyses(below).

Local extent–compositionLocal extent was represented by a ’window’ aroundeach used site and an equal set of random points, andthe window size was systematically varied by gradu-ally increasing the radius from 50 m to 500 m. Locallandscape composition considered the presence andquality of habitat features within the window, but nottheir explicit spatial configuration. For canopy struc-ture (i.e., available perch/nest trees) and water depth(i.e., access to foraging areas), the percentage of eachpatch type within each window was calculated usingFRAGSTATS (McGarigal and Marks 1995). Thesepercentages were subsequently weighted according tothe magnitude and sign of the chi-square statistic as-sociated with each patch type obtained in the MMUanalysis, then summed. The resulting index provideda measure of habitat quality within the window basedon its composition and eagle preference/avoidance ofpatch types. We assessed the difference in indexscores between used and random sites using a t-testfor group means, and repeated the analysis at eachradius distance. The magnitude of the Z-statistic wasused to evaluate the degree of habitat selection at each

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scale. Again, a Bonferroni adjustment of the criticalvalue was used to compensate for the number of com-parisons. Due to eagles’ extreme mobility, autocorre-lation due to the overlap of larger buffers was as-sumed to be irrelevant. However larger extents (> 500m) were not included due to potential problems withthis assumption.

For transitory human activities and shoreline de-velopment, we simply varied the buffer distance inthe negative logistic function (Figure 3) between 50m and 500 m and recomputed the disturbance indexdescribed previously (Sec. 2.3). As the buffer distancewas increased, more potential disturbances were in-

corporated into the disturbance index. For these anal-yses, a non-parametric ANOVA was used to assess thedifference in index scores between used and randomsites. The non-parametric procedure was necessarybecause of the highly skewed distribution of usedpatch types, particularly at finer scales. We repeatedthe analysis at each buffer distance and used the mag-nitude of the Z-statistic to evaluate the degree of hab-itat selection at each scale. Again, a Bonferroni ad-justment was used to control experimentwise error.

Figure 3. Scale-dependant patterns of breeding bald eagle habitat selection based on water depth at mean low tide for three elements oflandscape scale along the Hudson River, New York. (A) Relationship between the size of the minimum mapping unit (grain) and the degreeof habitat selection, represented by Cramer’s coefficient of association. (B) Relationship between minimum mapping unit and eagle selectionof individual habitat types, represented by partial chi-square values. Negative associations (habitat types used less than expected) are repre-sented as negative values. Larger chi2 values indicate greater differences between used and random sites. Statistical significance at the P =0.01 level was reached at chi2 = 11.34. (C) Relationship between the buffer radius, or size of the analysis window (i.e., local landscapeextent), and the statistical difference (Z-value) in habitat composition, or quality, between used and random forage locations. Statistical sig-nificance at P = 0.01 was reached at Z = 2.08. (D) Relationship between the buffer radius and the canonical discrimination (canonical R2)between used and random forage locations based on local landscape configuration.

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Local extent–configurationLocal landscape configuration considered the explicitspatial arrangement of habitat elements within the lo-cal window. Landscape configuration was only exam-ined for canopy structure (i.e., available perch trees)and water depth (i.e., access to foraging areas). Thedisturbance index values associated with transitoryhuman activities and shoreline development by defi-nition incorporated both landscape composition andconfiguration, because the distance from each humanactivity or developed structure to each focal cell wasconsidered, so additional analysis would have beeninappropriate. The configuration of the patch mosaicwithin each window was calculated using FRAG-STATS. A suite of landscape configuration metricswere initially selected based on knowledge of metricbehavior, insensitivity to window size, and knowl-edge of eagle ecology. Based on an analysis of mul-ticollinearity among metrics, a final suite of six un-correlated metrics were selected: patch density, edgedensity, largest patch index, area-weighted meanshape index, interspersion/juxtaposition index, andSimpson’s evenness index (McGarigal and Marks1995). We used discriminant analysis to evaluate thedegree of separation between used and random sitesbased on these six configuration metrics (Williams1981; McGarigal et al. 2000), and repeated the analy-sis at each radius. The magnitude of the canonical R2

and associated F-statistic was used to evaluate thedegree of separation between used and random loca-tions at each scale. Large canonical R2 values and F-values indicate distinct group separation or, in thiscase, strong differences between used and randomsites in landscape configuration. Variable loadings onthe canonical discriminant function were used to in-terpret the nature of the group differences (i.e., vari-ables with large loadings were primarily responsiblefor the observed differences between used and ran-dom sites).

Results

Over the two breeding seasons, we logged 430.5hours of direct visual observations of eagle activity.This resulted in the identification of 592 perch sitesand 58 forage locations. Many perch sites were usedrepeatedly. Breeding season home ranges for eaglepairs averaged 17.8 km2 (sd = 11.1 km2, range =5.14–35.32 km2); however, due to their affinity for

river shoreline, this translated into an average of 8.93linear river km (sd = 2.67 km, range = 5.0–10.6 km).

Access to foraging areas (water depth)

As expected, eagles exhibited strong selection for for-aging in shallow waters and areas exposed at low tide(i.e., tidal mudflats). With respect to grain (i.e.,MMU), eagles foraged in tidal mudflats more thanexpected (P < 0.001) at all grain sizes (Figure 3b).Overall chi-square values ranged between 88.8 and136.9, and increased nearly linearly as the spatialscale increased (Figure 3a), suggesting that althoughall tidal mudflats were used more than expected,larger mudflats were selected more frequently.

While selection did appear to increase as theMMU increased, the increase was slight and the over-all relationship among different patch types remainedconstant. The smallest grain size (0.09 ha) was there-fore selected as the resolution for the local extentanalyses to retain the maximum amount of spatial in-formation. At this grain, habitat selection based onlandscape composition was significant at smallerbuffer distances (Z = 3.5 at 40 m buffer, n = 107, P <0.001). This selection decreased non-linearly as thebuffer radius was increased, and revealed a threshold-like effect by leveling off at roughly 120 m (Figure3c). The canonical R2 statistics associated with land-scape configuration ranged between 0.505 and 0.148(Figure 3d). The higher R2 values at smaller spatialscales were driven by high coefficients of the area-weighted mean shape index metric values on the dis-criminant function (loading values = 0.941, 0.767,0.657 at 20 m, 40 m, and 60 m respectively).

Available perch trees (canopy structure)

As expected, eagles exhibited strong selection forperching in late-seral forest patches with irregularcanopies (i.e., high vertical heterogeneity), althoughthe strength of the association varied markedly withspatial scale (Figure 4). Specifically, eagle selectionfor perch sites based on canopy structure was great-est at the finest scales and decreased with increasingscale in all three analyses. With respect to grain, over-all chi-square values ranged from 194.44 at 0.01-haMMU to 18.98 at 23-ha MMU (Figure 4a). At finergrains, this pattern was driven by strong selection forpatches of late-seral irregular canopies (�2 = 134.4 at0.01 ha MMU) and avoidance of areas of early/mid-seral irregular canopies (�2 = 41.43 at 0.01 ha MMU)

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(Figure 4b). These results suggest that eagles selectedperch sites based on the canopy structure within verysmall patches. Even a single large tree or small standof large trees appeared to provide suitable perch sitesand attracted eagle use.

For local extent analyses, canopy structure wasmapped at a 0.01 ha MMU, corresponding to thegreatest selection in the grain analysis. Eagles se-lected perch sites based on both the composition andconfiguration of the landscape in the immediate vi-cinity of the perch site, consistent with the findingabove. Specifically, while eagles selected perch siteslocated in higher quality habitat than random sites(composition) at all spatial scales investigated, selec-

tion was most pronounced at relatively small (60–100m) analysis windows (Figure 4c). Thus, while the ex-tensiveness of good perching/nesting habitat in thevicinity of a perch site is generally important at allscales (i.e., the more the better), the signature is mostpronounced at relatively fine scales. Similarly, theconfiguration of suitable perch habitat patches withinthe local landscape context was most influential atfine scales (Figure 4d). The canonical R2 valuespeaked (0.41) at the smallest window size (20 m).However, in contrast to landscape composition, thediscrimination between used and random sites wasnot significant across all scales. In fact, the discrimi-nation was insignificant at all but the finest scales.

Figure 4. Scale-dependant patterns of breeding bald eagle habitat selection based on canopy structure for three elements of landscape scalealong the Hudson River, New York. (A) Relationship between the size of the minimum mapping unit (grain) and the degree of habitat se-lection, represented by Cramer’s coefficient of association. (B) Relationship between minimum mapping unit and eagle selection of indi-vidual habitat types, represented by partial chi-square values. Statistical significance at P = 0.01 was reached at chi2 = 11.34. (C) Relationshipbetween the buffer radius, or size of the analysis window (i.e., local landscape extent), and the statistical difference (Z-value) in habitatcomposition, or quality, between used and random perch locations. Statistical significance at P = 0.01 was reached at Z = 2.06. (D) Rela-tionship between the buffer radius and the canonical discrimination (canonical R2) between used and random perch locations based on locallandscape configuration.

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Moreover, the magnitude of discrimination, as judgedby the canonical R2, exhibited threshold-like behav-ior, decreasing sharply with increasing extent andthen abruptly leveling off after a 60- to 100-m bufferradius. The high R2 values at finer spatial scales weredriven by high coefficients of the area-weighted meanshape index values on the canonical discriminantfunction (loading value = + 0.885 and + 0.733 at 20m and 40 m, respectively).

Disturbance–transitory human activities

Not surprisingly, eagles exhibited strong selection forareas with less human activity at all spatial scales, al-though the strength and nature of the association var-ied markedly among spatial scales (Figure 5). Eagleselection for areas of low-moderate human activitywas most pronounced at coarse scales. With respectto grain, overall chi-square values ranged from137.51 at 2.25-ha MMU to 401.48 at 21.16-ha MMU(Figure 5b). However, two distinct patterns of scale-dependent selection were apparent. At finer grains (<4-ha MMU), selection was driven by less than ex-pected use of areas of none or moderate-high humanactivity (�2 = 96.2 and 79.9 at 0.25-ha MMU, respec-tively). Conversely, at coarser grains (> 4-ha MMU),selection was primarily driven by greater than ex-pected use of areas of low-moderate human activity(�2 = 309.32 at 22.1-ha MMU). This multi-scale re-sponse pattern, caused by avoidance of areas imme-diately surrounding disturbed areas and selection forlarge areas free from intensive human disturbance, isfurther evidenced by the somewhat bimodal responsein the overall chi-square value in figure 5a.

For local extent analyses, human activity wasmapped at a 21.16 ha MMU, corresponding to thegreatest selection in the grain analysis. A similar,though less obvious pattern emerged in the analysisof local landscape composition, with significant se-lection across spatial scales (Figure 5c). There waslittle variation in Z-values among spatial scales, rang-ing from 8.33 at a 200 m buffer to 10.808 at a 500 mbuffer. The slight drop in Z-values at the 200-m bufferdistance corresponds with the 4-ha pattern shift evi-dent in the analysis of grain (Figure 5a). Overall,these results suggest that eagles may avoid perchingwithin 200 m of moderate to high levels of humanactivities, yet are willing to tolerate some level of ac-tivity at greater distances.

Disturbance–shoreline development

Eagles also exhibited strong selection for areas withless shoreline development at all spatial scales, al-though there was again a distinct multi-scale response(Figure 6). With respect to grain, overall chi-squarevalues ranged from 135 at 0.01-ha MMU to 273.6 at23.04-ha MMU (Figure 6a). However, there was alsoa distinct peak in the chi-square value between 1.7-to 10.9-ha MMU. At all scales, the pattern of selec-tion was driven by greater than expected use of un-developed areas and less-than-expected use of areasof low to moderate development (Figure 6b). Themulti-scale response suggests that there may be twodifferent scales at which eagles respond to shorelinedevelopment, perhaps for different reasons. The firstselection peak, at 3.61-ha MMU, was selected as theresolution for the local extent analyses in order tomaintain the maximum amount of spatial information.

The difference between used and random perchsites based on the composition of the surroundinglandscape support our finding that eagles generallyselected areas with less shoreline development at allscales (Figure 6c). However, there was a distinctthreshold effect at approximately 150 m. Below thisthreshold, the amount of shoreline development in thevicinity of the perch site had relatively little influenceon site selection. Above this threshold, selection ofperch sites was strongly influenced by the amount ofshoreline development in the surrounding landscape.Note, this threshold is approximately equal to the ini-tial peak of selection observed at 1.7-ha MMU in theanalysis of grain (Figure 6a).

Discussion

Eagle Habitat Selection along the HRC

Breeding eagle selection of habitat along the HRCwas generally consistent with past eagle research, al-though the degree and nature of the selection was in-fluenced by spatial scale. Eagles’ selection of tidalmudflats as foraging areas was consistent among spa-tial scales, with a near linear relationship between thesize of the mudflat and the degree of selection. How-ever, eagles also appeared to select foraging areas onthe basis of fine-scale variations in depth, as revealedby the composition and configuration analyses. Thisapparent inconsistency between the grain and localextent analyses revealed additional details regarding

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eagles’ selection of foraging areas. Large mudflats of-ten contain a complex system of pools and drainagechannels; details that are lost as the MMU is in-creased. Both the compositional and configurationanalyses indicated that within large mudflats, eaglesmost often foraged in areas associated with fine-scalevariation in depth. This conclusion is supported by thehigher area-weighted mean shape index values atused sites versus random sites. The area-weightedmean shape index can be interpreted as a standard-ized index of patch edge density adjusted by patchsize; the higher values at used sites indicates that ea-gles selected foraging areas with more patch edge inthe immediate vicinity (i.e., high local variability inwater depth) than random sites. Our observations onforaging behavior provide a plausible explanation for

this pattern. Specifically, that as the tide recedes, fishremaining on the mudflat are forced into drainagechannels and pools, providing easy foraging opportu-nities. Thus, eagles appeared to select areas of highlocal variability in water depth, perhaps due to in-creased foraging effectiveness, within larger mudflatcomplexes that offer more foraging opportunities thansmaller mudflats.

Eagles are predominantly ambush, sit-and-waitpredators; only a few suitable perches are required forsuccessful foraging and they may be used repeatedly(McGarigal et al. 1991). Eagle perch sites are tradi-tionally associated with older, dominant trees withopen flight paths and wide views (Stalmaster 1987).In our study, although eagles selected this type ofcanopy structure at all scales, they showed much

Figure 5. Scale-dependant patterns of breeding bald eagle habitat selection based on human activity for two elements of landscape scalealong the Hudson River, New York. (A) Relationship between the size of the minimum mapping unit (grain) and the degree of habitat se-lection, represented by Cramer’s coefficient of association. (B) Relationship between minimum mapping unit and eagle selection of indi-vidual habitat types, represented by partial chi-square values. Statistical significance at P = 0.01 was reached at chi2 = 15.09. (C) Relationshipbetween the buffer radius, or size of the analysis window (i.e., local landscape extent), and the statistical difference (Z-value) in habitatcomposition, or quality, between used and random perch locations. Statistical significance at P = 0.01 was reached at Z = 2.06.

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greater selection at the finest scales (Figure 4). Spe-cifically, eagles appeared to select perch sites basedon the characteristics of individual trees or smallclumps of trees, preferring areas of high vertical can-opy relief (i.e. forest edges). This may also explainwhy the discrimination between used and randomperch sites was significant only at the finest scales.The vertical structural relief that patch edges providemay have no influence on an eagle’s use of an indi-vidual perch tree beyond 20–40 m.

With respect to human disturbance, eagles showeda variety of responses. At fine spatial scales, eaglesavoided both areas of moderate to high human activ-ity as well as areas of no human activity, but perhapsfor very different reasons. Eagles are extremely sen-sitive to the proximity of human activities and gener-

ally avoid contact with humans (McGarigal et al.1991). However, the predominant human activityalong the HRC is recreational fishing, which is gen-erally concentrated in productive areas (e.g., sub-aquatic vegetation beds, areas of submerged debris,etc). Areas of no human activity relate to unproduc-tive fishing areas such as deeper water or faster cur-rent, and are therefore avoided by eagles and fisher-men alike. At coarser spatial scales, eagles preferredlarge tracts of undeveloped shoreline with low tomoderate human activity. Along the HRC, eagles ap-pear to tolerate low levels of human activity in returnfor access to productive foraging areas. This is simi-lar to the response reported by McGarigal et al.(1991) along the Columbia River, where eagles werefound only to approach boats when the probability of

Figure 6. Scale-dependant patterns of breeding bald eagle habitat selection based on shoreline development for two elements of landscapescale along the Hudson River, New York. (A) Relationship between the size of the minimum mapping unit (grain) and the degree of habitatselection, represented by Cramer’s coefficient of association. (B) Relationship between minimum mapping unit and eagle selection of indi-vidual habitat types, represented by partial chi-square values. Statistical significance at P = 0.01 was reached at chi2 = 18.48. (C) Relation-ship between the buffer radius, or size of the analysis window (i.e., local landscape extent) and the statistical difference (Z-value) in habitatcomposition, or quality, between used and random perch locations. Statistical significance at P = 0.01 was reached at approximately Z = 2.06.

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successful foraging was high. Stalmaster and New-man (1978) also found that eagles developed toler-ances to repeated activities, although Fraser et al.(1985) reported the opposite.

Scale-dependant habitat selection

Over the range of spatial scales we examined, eaglehabitat selection appeared to be driven by multiplechoices at a variety of spatial scales. Habitat selec-tion was differentially influenced by spatial scale bothbetween different habitat components (e.g., canopystructure vs. water depth) and within components(e.g., increased selection at finer scales for some com-ponents). Distinctive scale-dependent patterns wereevident in all four of the habitat components ana-lyzed, including critical thresholds and multi-scale se-lection. While no statistical tests of differences be-tween habitat components were conducted,differences in scale-dependant patterns were easilyidentified (Figures 3, 4, 5 and 6).

Critical thresholdsCritical thresholds in landscape ecology have beendefined as transition zones where a small change inlandscape structure can produce relatively largechanges in the associated ecological response (Turnerand Gardner (1991) and With and Crist (1995) andGardner (1997, p. 219). In terms of wildlife-habitatrelationships, this is often thought of in terms of hab-itat connectivity Turner et al. (2001, p. 234) or spe-cies persistence (Lamberson et al. 1992). The ecolog-ical importance of such thresholds depends on thehabitat feature being evaluated, the scale of the eval-uation, and the organism’s dispersal abilities. For ex-ample, Kerkhoff et al. (2000) found that Florida pan-thers were unable to survive in areas where the forestcover fell below approximately 25% and Gibbs(1998) reported that the persistence of three woodlandamphibian species was directly tied to thresholds offorest fragmentation.

We observed several threshold-like patterns ofhabitat selection by HRC eagles. With respect to ac-cess to foraging areas, the selection of specific forag-ing locations within large tidal mudflats was greatestat the finest extent (i.e., 50-m radius window aroundforage locations) in both the composition and config-uration analysis (Figuress 3c and 3d). This selectiondecreased non-linearly with increasing extent, reveal-ing a threshold at roughly 120-m. This threshold maycorrespond to the overall area a foraging eagle is

searching at any given time (i.e., its �ecologicalneighborhood�, sensu Addicott et al. (1987)), regard-less of a preference for larger tidal mudflats. Simi-larly, we observed a distinct threshold in selection ofperch locations based on canopy structure. Below a4-ha resolution, eagles showed strong selection forlate-seral forest stands with an irregular canopy (Fig-ure 4b). While this preference was also present atlarger scales, the degree of selection fell by approxi-mately eighty percent.

Wiens (1989) described a unique form of scalingthresholds, where two critical thresholds define arange, or ’domain’, of biologically appropriate re-search scales. Within this range, organisms displaypatterns of selection that either remain constant orchange along predictable patterns. We observed evi-dence of such a range in eagles’ response to shorelinedevelopment. Between 2- and 10-ha MMU resolu-tion, eagles displayed a relatively constant sixty per-cent increase in selection for undeveloped shorelineover finer and coarser resolutions (Figure 6b).

Multi-scale selectionNot only have researchers found that scales of habitatselection vary among taxa (Pearson and Gardner1997; Turner et al. 2001), but there is also evidenceof variation within taxa. For example, Turner et al.(1997) found that winter foraging by Yellowstone un-gulates depended on decisions made at multiple spa-tial scales. Eagles along the HRC showed such hier-archical, multi-scale selection for several landscapeelements, including both human activity and shorelinedevelopment.

With respect to transitory human activity, eaglesshowed the greatest sensitivity to disturbances within150–200 m. Within this initial threshold distance, ea-gles avoided areas of moderate to high human activ-ity. Beyond this distance, the degree of sensitivitydecreased quickly, and eagles increasingly selectedareas of low to moderate human activity, presumablytolerating human activity in return for increased for-aging efficiency associated with the more productivefishing sites. It is important to note that our study wasrestricted to spatial information; no effort was madeto monitor temporal overlap between eagle and hu-man use of areas. As a result, any temporal avoidanceof recreational fishing areas would have been missed.Stalmaster and Kaiser (1998) noted that wintering ea-gles were quick to resume feeding after being dis-turbed, but that this response slowed with repeateddisturbances. Similarly, McGarigal et al. (1991) noted

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a temporal shift in foraging activities in response tohuman activities in preferred foraging areas. Theseand other authors (e.g., Steidl and Anthony (1996))have in fact suggested that temporal control of humanactivity around eagle use areas may be an effectivemanagement strategy in some cases.

Eagles exhibited a similar multi-scale response toshoreline development, with a distinctly bimodal se-lection function over the range of grain sizes investi-gated (Figures 6a and 6b). We speculated that alongthe HRC eagles are influenced by shoreline develop-ment differentially according to perch locations.When perched on undeveloped islands and protectedby open water, eagles are more tolerant of develop-ment but prefer to remain at least 150–200 m distant.When perched on the primary shoreline without thebenefit of an open water buffer, eagles are less toler-ant of development and select larger stretches of un-developed shoreline.

Scope and limitations

Several factors limit our ability to generalize our find-ings. First, our study was limited to only four pairs ofbreeding bald eagles. At the time we conducted ourresearch, this represented the entire HRC breedingbald eagle population. Small sample sizes are typicalof endangered species research; nonetheless, this sys-tem offered the opportunity to investigate scale-de-pendent habitat selection in a limited environment.The goal of this paper was not to support the signifi-cance of eagle habitat requirements, the importanceof which have long been established, but to test forscale-dependent patterns in relationships of provenimportance in a limited environment, and to exploremethodologies for the identification of these patterns.In this context, concerns over sample size are some-what reduced.

Second, although our primary objective was to in-vestigate eagle habitat selection over a range of spa-tial scales, the range of scales investigated was lim-ited to what has been called ’local’ scales (Wiens etal. 1987). Therefore, our conclusions are limited tolocal eagle habitat use along the HRC. It is almostcertain that other, equally important, patterns exist atbroader (e.g., regional) scales.

Third, the fact that eagles showed significant habi-tat selection at almost all spatial scales is potentiallymisleading and does not undermine the importance ofthe scale-dependent patterns of selection we ob-served. In this study, we initially selected habitat

components based on their well-documented impor-tance to breeding eagles to test for scale-dependantpatterns. As a result, our working hypothesis was thatselection would be significant across a wide range ofscales, but that the magnitude of significance wouldvary with scale. Indeed, our results largely supportthis hypothesis. It can be expected that in studieswhere researchers have less a priori information onhabitat relationships, the range of selection statisticswill be greater. In such situations the need for scale-sensitive research is even greater as important effectscould be missed unknowingly.

Finally, our analysis of local landscape extent wasdesigned to include various aspects of landscapestructure, including both the composition and config-uration of habitat patches within the surroundinglandscape. However, the analysis of landscape con-figuration may have suffered from methodologicallimitations associated with applying configurationmetrics to relatively small landscapes related to ourassignment of random points. Initially, we restrictedthe selection of random points to a shoreline bufferthat included 95% of observed perch or forage loca-tions. However, used locations were not evenly dis-tributed throughout this area, instead being heavilyconcentrated on the shoreline itself. At fine scales ofanalysis (e.g., < 100 m buffer radius), the used land-scapes were typically one habitat type cut by a shore-line edge, while random landscapes were often onecircular habitat patch without the shoreline edge.Therefore, at fine scales the shape of used and ran-dom landscapes differed markedly. While this effectdid not influence the grain or landscape compositionanalyses, it did affect the FRAGSTATS shape indicesused in the configuration analysis, inflating the differ-ence between used and random points and increasingthe canonical discrimination at fine scales. As thescale increased, this effect disappeared as more ran-dom landscapes began to include shoreline edge.Hence, the results of our landscape configurationanalysis must be interpreted cautiously. In future ap-plications of this methodology, some type of stratifiedrandom point selection mimicking the distribution ofused points may be more appropriate when usinglandscape shape metrics in the presence of a domi-nant landscape edge such as shoreline.

Conclusions

The identification of an ’organism-centered perspec-tive’ is a complex task. In order to properly evaluate

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habitat selection or quality, wildlife managers need tounderstand and mimic selection at multiple spatialand temporal scales. In our study, which had the ben-efit of extensive past research defining critical eaglehabitat requirements, we were able to identify threeways in which the use of scale-sensitive research mayincrease our ability to evaluate habitat. First, the iden-tification of so-called ’thresholds’ in scale-dependantselection patterns may indicate the boundaries of bio-logically appropriate research scales, or ’domains ofscale’ (sensu Wiens (1989)). The use of these thresh-olds to define research scales will both improve man-agement and facilitate comparisons between studies.In our study, for example, such a threshold was ap-parent in eagles’ reaction to canopy structure (Fig-ure 4), suggesting that eagles select perch sites basedprimarily on individual tree rather than stand charac-teristics. Second, organisms may respond to spatialheterogeneity and patterning at multiple scales (Turn-er et al. 2001). In our study, eagles showed differentscale-dependent patterns of selection to different re-sources, particularly canopy structure and human ac-tivity. An accurate ’organism-centered perspective’,therefore, must assess resources independently and ata range of scales. Third, organisms may also showmulti-scale responses to a single resource. Along theHRC, eagles appeared to respond to human activityalong two primary axes. At finer spatial scales, theyavoided potential disturbances, but at coarser scalesthey showed strong selection for areas of low-moder-ate human activity and avoided areas of no activity(i.e., also unproductive foraging areas), indicatingsome degree of tolerance in exchange for foragingsuccess. An accurate habitat evaluation must accom-modate both responses.

With the gradual integration of the landscape per-spective into wildlife ecology, a broader understand-ing of the term ’habitat’ has evolved. In referring tothe �fabric� of spatial scales, Wiens (1976) initiallysuggested the multi-dimensional complexity inherentin scale-sensitive research. Tied to the understandingof the importance of spatial scaling is the realizationthat a ’habitat patch’ is merely an artifact of the scaleof analysis. As the scale shifts, landscape patterns alsoshift, changing habitat boundaries, availability, anduse. It is therefore no surprise that many past habitatstudies, conducted on the same species at differentscales, gave different results. For example, eaglesalong the HRC displayed several independent, scale-dependant responses. Habitat evaluations conductedat fine scales would have emphasized the importance

of canopy structure and solitude, while those con-ducted at coarser scales would have focused on apreference for areas of low-to-moderate human activ-ity and ignored the importance of available perchsites. Only with a multi-scale approach do these pat-terns become ecologically interpretable. In order tounravel the influence of spatial heterogeneity to wild-life, Turner et al. (2001, p. 228) emphasized the needboth to understand an organism’s multi-scale responseto it’s environment and to realize that this responseoften differs from our own. The challenge for wild-life ecologists is now to think of habitat as a spec-trum, or fabric, with patterns that shift in relation tospatial or temporal scales.

Acknowledgements

This research was conducted under the auspices of theInstitute for Wildlife Studies, Arcata California, USAthe New York Department of Environmental Conser-vation Endangered Species Unit (NYSDEC), and theDepartment of Natural Resources Conservation, Uni-versity of Massachusetts. We are grateful to Bill Mc-Comb, Curt Griffin and Jack Ahern, as well as twoanonymous reviewers, for comments on earlier draftsof this manuscript. Funding was provided by theNYSDEC Hudson River Estuary Program, the NOAAEstuary Research Reserves Graduate Fellowship pro-gram, and the Hudson River Foundation Tibor T. Pol-gar Fellowship program.

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