limitations of using landscape pattern indices to evaluate … · limitations of using landscape...

Landscape and Urban Planning 72 (2005) 265–280

Limitations of using landscape pattern indices to evaluate theecological consequences of alternative plans and designs

Robert C. Corrya,∗, Joan Iverson Nassauerb

a School of Environmental Design and Rural Development, University of Guelph, Guelph, Ont., Canada N1G 2W1b School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109-1115, USA

Received 11 August 2003; received in revised form 28 January 2004; accepted 15 April 2004

Abstract

Landscape pattern indices have been discussed as tools for comparing the ecological consequences of alternative landscapeplans and designs. While several landscape ecology investigations have demonstrated the applicability of pattern indices forcharacterizing landscapes, there is a lack of evidence that pattern indices imply ecological processes. We investigated the utilityof landscape pattern indices for judging the habitat implications of alternative landscape plans or designs, and we compared ourresults to our colleagues’ application of a spatially explicit population (small mammal) habitat model for the same landscapeplans. Results of our investigation suggest that planners and designers should be exceedingly cautious in making ecologicalinferences from landscape pattern index values applied to alternative landscape plans. For our high resolution data representinghighly fragmented landscapes, indices were of limited utility in measuring pattern differences, and while reliable, were not validfor measuring differences in an ecological consequence: small mammal habitat.©

K

1

ska

f

hoseem-

tionrna-usedpesandsure-Ex-

nnedex-

tion

0

2004 Elsevier B.V. All rights reserved.

eywords:Landscape indices; Small mammals; Ecological consequences

. Introduction

The act of planning and designing future land-capes is a creative endeavour that draws upon scientificnowledge. Each resulting plan or design can be char-cterized as an embodiment of hypotheses about the

∗ Corresponding author. Tel.: +1 519 824 4120x58034;ax: +1 519 767 1686.

E-mail address:[email protected] (R.C. Corry).

effects of landscape change (Nassauer, 1995). Tthat plan and design future landscapes rely uponpirical evidence, modeling outcomes, and intuito achieve desired ends and judge among altetives (Laurie, 1975). These approaches can beto develop more ecologically beneficial landsca(Ahern, 1991; Opdam et al., 2001). To evaluatechoose among alternative landscape plans, meament of existing landscapes may not be sufficient.isting landscapes may not be comparable to plafuture landscapes, or measuring functions of largeisting landscapes may be too costly. Experimenta

169-2046/$20.00 © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.landurbplan.2004.04.003

266 R.C. Corry, J.I. Nassauer / Landscape and Urban Planning 72 (2005) 265–280

with small landscapes, while informative, has limitedgeneralizability to larger areas (Wiens et al., 1995;Collinge, 1996; Collinge and Forman, 1998; Mabryand Barrett, 2002). Without adequate tools to anticipateecological outcomes, plans and designs that are imple-mented may be either untested or relatively unimagi-native, simply repeating landscape patterns that havebeen proven by the test of time. Landscape ecologi-cal theory has opened doors to innovation in landscapeplanning and design (Nassauer, 1999). Now planners,designers, and stakeholders need efficient tools to quan-titatively evaluate alternative plans and designs andmake informed choices among them (Jongman, 1999;Opdam et al., 2001; Botequilha Leitao and Ahern,2002).

Landscape pattern indices have been discussed asa way to address this need (Botequilha Leitao andAhern, 2002). Landscape pattern indices are measuresfor quantifying the composition and configuration ofecosystems across a region or study area (landscapepattern indices are commonly also called “metrics”).They have been used to characterize different studyareas or regions (O’Neill et al., 1988; Turner andRuscher, 1988) or to measure a region’s landscapechange through time (Dunn et al., 1990; Delong andTanner, 1996; Reed et al., 1996). Indices might bea way to evaluate and compare the before-and-afterconditions offered by a landscape plan, or might bea way to evaluate alternative plans for a particularlandscape (Gustafson, 1998; Botequilha Leitao andA asi-e atet pat-t al.,2 tiono acter-i rna-t esteda an,1 ta icalr gratet ndetd

land-s pre-

dict ecological consequences (Tischendorf, 2001) andthat indices are effective evaluative tools for plan-ning and design (Botequilha Leitao and Ahern, 2002).Botequilha Leitao and Ahern (2002)provide five rec-ommendations for the use of landscape pattern indicesin sustainable landscape planning:

(a) the use of indices at several phases of planning anddesign;

(b) using a core set of landscape pattern indices thathave been suggested through previous research;

(c) using indices for comparative purposes (e.g., in-forming choices among alternatives);

(d) relying upon more than one index from the core setof indices to reveal information about ecologicalprocesses; and,

(e) using indices to provide useful directions for plan-ning and design (pp. 88–89).

They qualify these recommendations, indicating theneed for more research that focuses on the relationshipbetween pattern and process at several scales, the inte-gration of spatial tools, and the selection of landscapepattern indices for particular cases.

Landscape pattern indices have two potentially at-tractive attributes for planners and designers: they arerelatively efficient tools that can be applied quickly toseveral different alternative plans (as opposed to morecomplex models that may have prohibitive computingrequirements, expensive calibration requirements, orbe discipline-centered) (Turner et al., 1995); and theya ted,a tivep s,h beu andt e.g.a lass,o blei canr im-p lano

mustb caleo pre-t noti son,1 ate

hern, 2002). Landscape experimentation or “quxperimentation” could be a useful way to investighe effectiveness of evaluative tools like landscapeern indices for planning and design (Opdam et001). Indices may be useful as a first approximaf broad-level landscape pattern and process, char

zing differences among planned or designed alteives (Jongman, 1999), and they have been suggs a suitable tool for planning and design (Jongm999; Botequilha Leitao and Ahern, 2002).Opdam el. (2001)identified a need for landscape ecologesearch to investigate methods and tools to intehe application of ecological principles for design avaluation.Botequilha Leitao and Ahern (2002)echohis call to focus “practice-oriented research. . .to theevelopment of operational tools” (p. 89).

Most recently, research has suggested thatcape pattern indices may be able to effectively

re accessible tools, easily acquired, fully documennd applicable to digital data representing alternalans and designs (e.g., in agis). These same attributeowever, may allow landscape pattern indices tosed incorrectly. Dozens of indices are available,

hey can be applied at several different levels (cross the landscape, or for a particular land cover cr for a selected patch). Applying the many availa

ndices without a priori hypothesizing relationshipsesult in a “fishing expedition” (Gustafson, 1998), sly looking for the indices that confirm the desired putcome.

To be useful to planners and designers, indicese both reliable and valid for landscapes at the sf design decision-making. The meaning and inter

ation of index values in any given application isntrinsic to any landscape pattern index (Gustaf998; Turner et al., 2001). Investigations to elimin

R.C. Corry, J.I. Nassauer / Landscape and Urban Planning 72 (2005) 265–280 267

redundancy and to select indices that provide unique in-formation about common landscape patterns are ongo-ing (O’Neill et al., 1988; Gustafson, 1998; Hargis et al.,1998; O’Neill et al., 1999). When an index has the samenumerical value for dramatically different landscapes(e.g.,Tischendorf, 2001), or erratic behaviour undersome landscape conditions (e.g., responding to the pro-portion or distribution of patches;Schumaker, 1996; Heet al., 2000), its diagnostic value is limited. The scaleof index application is critical to the inferential value offindings (Thompson and McGarigal, 2002). In a studyof habitat use in bald eagles (Thompson and McGari-gal, 2002), for example, investigators found that ea-gles have a multi-scale habitat response, using bothcoarse-scale (e.g., lack of human disturbance) andfine-scale (e.g., perch and feeding location) habitatcues, and that landscape pattern indices yield dif-ferent conclusions when applied to different extentsand resolutions of spatial data. Certainly, indices can-not characterize landscape ecological functions with-out a clear understanding of the relationship be-tween index values and ecological processes acrossscales.

Applying indices to landscape plans and designsmay pose challenges that are less relevant to other ap-plications within landscape ecology. The fine scalestypical of landscape designs and plans may pose alimitation in the application of landscape indices. In-dices have most commonly been evaluated for theirbehaviour on relatively coarse-resolution spatial data( rd m),y pa-t apei im-p s ofm cano soci-a rn –h dingt witht rop-e efult rn in-dp en atfi tchp

A substantial shortcoming associated with applyinglandscape pattern indices is that indices have not beenexplicitly linked with ecological processes: that is, thelandscape pattern that can be quantified with indicesmay differ from the landscape pattern that is ecologi-cally relevant to the resources being managed (Turner,1989; Turner et al., 2001). The recentThompson andMcGarigal investigation (2002)of the relationship be-tween scale and species response may provide a par-tial explanation as to why the link between an indexvalue and an ecological process is incomplete: differ-ent scales of study yield different results.

We investigated the reliability and validity of land-scape pattern indices as tools for planners and de-signers by applying a selected set of indices to judgea particular ecological consequence, small mammalhabitat quality, of alternative designed landscape fu-tures for Corn Belt agriculture in two Iowa watersheds(Nassauer et al., 2002). This Corn Belt landscape dataset shared relevant characteristics with other highlydisturbed, settled landscapes. Our data represented ahighly fragmented landscape in which patches of goodhabitat were relatively small (0.16–10.64 ha), and thedata were at a high resolution (3 m). To assess reliabil-ity and validity we compared our results to publishedstudies, we compared different indices to each other,and we compared our results to our colleagues’ spa-tially explicit population model (sepm) results for thesame landscape futures.

We selected small mammal habitat quality as a targete haved ord tot smallp nd-s lese rep-r or ours t thel tchesa ought andB sheda logyi s-o enao mallmt

e.g., 10 to 200+ m data resolution;Table 1). Plans oesigns are often proposed at fine resolutions (∼1et the behaviour of indices for fine-resolution sial data is almost completely unknown. Landscndices that can describe fine-scale patterns will beortant to characterizing the designed landscapeetropolitan areas. Coarse-resolution spatial databscure common landscape patterns that are asted with cultural dimensions of landscape patteuman land division and settlement patterns, inclu

he small patches of potential habitat associatedhe roadsides, railways, field boundaries, and prty lines (Corry and Nassauer, 2002). To be us

ools for planners and designers, landscape patteices need to providereliable results thatvalidly im-ly something about ecological consequences evner scales, including narrow linear and small paatterns.

cological consequence because small mammalsispersal distances and home range sizes that acc

he scale of the watershed study areas, and theatches of habitat common to highly fragmented lacapes (Table 2) (Danielson and Anderson, 1999; Pet al., 1999). Our 3 m resolution data allowed us toesent the landscape at a scale that was relevant fpecies of interest. That is, small mammals detecandscape a few metres at a time, use small pas habitat, and are capable of short dispersals thr

he landscape matrix (Wegner et al., 1999; Mabryarrett, 2002). We used 3 m data according to publidvice on the selection of scale for landscape eco

nvestigations:O’Neill et al. (1996)suggest a data relution that is 2–5 times smaller than the phenomf interest, and the smallest home range size of sammals that we considered was 50 m2, or just more

han five grid cells (Table 2).


Table 1Comparison of reported values to published values for selected landscape indices (sorted by data resolution)


Table 2Target mammal species and biological parameters (adapted fromClark et al., submitted)

Species name Common name Home range Dispersal(hectares) distance (m)

At-ground nesting speciesBlarina brevicauda Short-tailed shrew 0.59 60Cryptotis parva Least shrew 1.2 270Microtus ochrogaster Prairie vole 0.05 320Microtus pennsylvanicus Meadow vole 0.040 280Peromyscus leucopus White-footed mouse 0.081 430Peromyscus maniculatus Deer mouse 0.6 500Sorex cinereus Masked shrew 0.04 280Synaptomys cooperi Southern bog lemming 0.06 110

Below-ground nesting speciesPerognathus flavescens Plains pocket mouse 0.005 70Marmota monax Woodchuck 3.1 300Zapus hudsonius Meadow jumping mouse 0.6 500

2. Hypotheses

To test index validity, we used a multi-method ap-proach (Robson, 1993) comparing our results for re-lated landscape indices, and also comparing our indexresults to our colleagues’ parallelsepm evaluation of thesame futures (Clark et al., submitted). To evaluate indexreliability, we compared our index values with valuesreported in the literature for other, comparable studyareas. We hypothesized that our index values would becomparable to published values, that our selected in-dices should be correlated in many cases (e.g., as theproportion of habitat increases, the distance betweenhabitat patches should decrease; functional connectiv-ity indices should indicate increased habitat proxim-ities), and that our index values would be correlatedwith sepm results (Clark et al., submitted). Since asepmparameterizes synthesized expert knowledge, it is onerepresentation of the habitat value of our study areasthat could provide data for a test of the external valid-ity of the indices (Dunning et al., 1995; Turner et al.,1995).

To select appropriate landscape indices, we re-viewed literature that has suggested the value of smallpatches, indigenous habitats, and diverse landscapesfor other small-bodied organisms, such as arthropods(Thomas et al., 1991; Landis and Haas, 1992; Thomaset al., 1992; Marino and Landis, 1996; Orr andPleasants, 1996; Colunga-Garcia et al., 1997; Pleasantsand Bitzer, 1999), and landscape pattern implicationsf ing

a basis for estimations of small mammal habitat qualityin the alternative landscape futures.

Reviewing the literature, we identified four at-tributes of landscape pattern to which small mammalswere expected to respond: proportion of habitat in thelandscape (i.e.,howmuch habitat is there?), landscapegrain size (i.e.,how big are the patches in the land-scape?), landscape diversity (i.e.,how many habitattypes are there?), and functional connectivity amonghabitat patches (i.e.,how clumped or dispersed arethe good habitat patches?). Each of these pattern at-tributes could be quantified by one or more landscapepattern indices, and we hypothesized the reported val-ues of indices to rank the futures in the same orderas our literature-based estimations (Table 3). For ex-ample, we expected that as landscape diversity in-creased, small mammal populations would be moreviable, just as literature has suggested for arthropodsin the Corn Belt (Landis and Haas, 1992; Marino andLandis, 1996; Colunga-Garcia et al., 1997). We also hy-pothesized that as landscape grain became increasinglyfine, small mammal habitat quality would improve – ahypothesis unique to highly fragmented contexts wherelarge patches are commonly poorer habitat than smallpatches (Schwartz and van Mantgem, 1997; Ludwig,1999; Corry and Nassauer, 2002).

We hypothesized that different landscape indicesintended to represent the same specific attribute oflandscape pattern would be correlated. For example,indices of functional connectivity should demonstratea dex
or small mammals (Barrett and Peles, 1999) provid predictable relationship, such that when one in


Table 3Pattern attributes, indices, and hypotheses for small mammals in Corn Belt Iowa

Landscape pattern attribute Landscape index Generalized hypothesis

Proportion of good habitat Proportion of landscape (PLAND) More good habitat is better; less poor habitat is betterLandscape grain size Mean patch size (MPS) Fine-grained landscape is betterClustering of habitat types Interspersion and juxtaposition index (IJI);

aggregation index (AI); contagion (CONTAG);Euclidean mean nearest neighbour (MNN)

Good habitat should be clustered, poor habitat shouldbe dispersed

Landscape diversity Relative patch richness (RPR); modified Simpson’sevenness index (MSIEI)

Heterogeneity is better than homogeneity

of connectivity implies greater clumping of habitats,another index of functional connectivity should alsoimply a similar direction and magnitude of habitatclumping. We also expected to find many correlationsamong different pattern attributes. As proportion ofgood habitat in the landscape increases, for example,we expected that the distance between these habitatpatches would decrease (e.g., an inverse correlationbetween the proportion-of-landscape measure and themean nearest neighbor measure).

This paper describes the methods of our investiga-tion, the results of applying indices, the evaluations ofreliability and validity, and our conclusions about theusefulness of landscape pattern indices for planningand design. Our conclusions include advice for plan-ners and designers who use or critique the use of indicesto infer ecological consequences.

3. Methods

As part of a broad interdisciplinary research project(Santelmann et al., 2001; Nassauer and Corry, in press),futures for three alternative landscape scenarios for twoCorn Belt agricultural watersheds were represented ina geographic information system (gis) at 3 m resolution(Nassauer et al., 2002) (Fig. 1). We used the baselinelandscape conditions and the alternative future land-scape plans to test landscape pattern indices as evalua-t archp nd-s eralea al.,s onm al.,s

Corn Belt agriculture is a highly fragmented land-scape type (Schwartz, 1997). The largest patches arecorn and soybeans, and good habitat is infrequent andsmall (Farrar, 1981; Schwartz and van Mantgem, 1997;Bernstein, 1998; Bishop et al., 1998; Smith, 1998;Corry and Nassauer, 2002). The two Corn Belt water-sheds that were our baseline study areas are describedelsewhere in greater detail (Nassauer and Corry, 1999;Santelmann et al., 2001; Nassauer et al., 2002). Bothof the watersheds (87 and 56 km2 in area) are typicalof Iowa’s agricultural landscape with large fields ofrow crops, and relatively small patches of high-qualityhabitat (Fig. 2) (Nassauer et al., 2002).

Beginning with baselinegis coverages for the twowatersheds, we developed landscape futures at 3 m res-olution for three scenarios. Designs include fine-scalefeatures such as field boundary vegetation, roadsides,and grassed waterways, and narrow strip intercropping(Cruse, 1990). We applied the landscape pattern in-dices (Table 3) to the alternative futuresgis data us-ing fragstats (version 3.0b) (McGarigal and Marks,1995) for all indices except the aggregation index,which was calculated with software named “ai” (Heet al., 2000).

3.1. Data conversion

Three distinct data conversion procedures affectedour analysis and interpretation of landscape patternm erizea calel e: (a)c ter;( rip-t (c)a weren ices

ive tools for planning and design. In the broad reseroject, collaborators evaluated the alternative lacape scenarios with disciplinary methods for sevcological endpoints (e.g.,Coiner et al., 2001; Vache etl., 2002; Rustigian et al., 2003; Santelmann etubmitted), including a spatially explicit populatiodel (sepm) of mammal response (Clark et

ubmitted).

easurement. Similar procedures would charactny application of landscape indices to fine-s

andscape plans or designs. The procedures aronversion of spatial data types from vector to rasb) conversion of classification system from descive agricultural classes to habitat classes; andggregation of habitat classes. Such proceduresecessary for application of landscape pattern ind


Fig. 1. Land cover maps for alternative Corn Belt landscape patterns for the Buck Creek (a–d) and Walnut Creek (e–h) watersheds, Iowa (note:scale of watersheds differs for legibility).


Fig. 2. Corn Belt, Iowa agricultural landscape (photo: R. Corry).

and interpretation of index values, but data conversionand aggregation inherently changes landscape repre-sentation. The data type conversion procedure fromvector to raster data “dissolved” boundaries betweenadjacent, contiguous patches of the same type, makingthem appear as one larger patch (e.g., a patch of wood-land “good” habitat next to a patch of fencerow “good”habitat would be dissolved into one large “good”habitat patch). In addition, to facilitate a comparison tothesepm for small mammals, we converted land coverclasses from generally descriptive types (e.g., corn) tohabitat classes consistent with our colleagues’ method(i.e., that incorporated biological parameters). Wefurther aggregated our colleagues’ five distinct habitatclasses to three classes to make index interpretationconsistent with conventions for habitat interpretationthat we discuss below. While data type conversion andclass aggregation is common togis analysis of landcover data and may be necessary for application ofsome indices, data type and classification affects indexmeasurements. Below, we detail these effects.

The spatial data for the baseline land cover dataand alternative future plans were converted fromvector data to raster data for our colleagues’sepmmodels and for our application of landscape pattern

indices (some indices cannot be applied to vectordata). As a consequence, unique patch boundarieswere dissolved – meaning that two contiguous patchesof the same type that were spatially distinct in thevector representation would be represented as onepatch in the raster representation. The dissolved patchboundaries affected landscape pattern index values(especially indices of landscape configuration).

We converted land cover classifications to use thehabitat classes employed in thesepm for small mam-mal habitat quality. For example, land covers labeled“alfalfa” and “pasture” may not differ in small mammalhabitat effects. To reduce the 87 classes that came fromthe baselinegis data to habitat-specific classes (i.e., re-ferring to habitat quality of patches), we relied upon ourcolleagues’ expert assessment of small mammal habitatquality of land covers that were included as parametersin theirsepm (seeTable 4) (Clark et al., submitted); thisconversion supported our validity test, comparing ourlandscape pattern indices to the spatially explicit pop-ulation model. To make interpretation of index valuesconsistent with conventions, we aggregated the five dif-ferent classes of habitat quality used by our colleaguesin their sepm to three classes using the followingrule:


Table 4Rule-derived habitat classification for two guilds of small mammal species (land cover types from (Clark et al., submitted))

Land cover type Habitat quality for at-groundnesting mammals

Habitat quality for below-groundnesting mammals

Row crop Poor PoorNo-till crop Poor PoorStrip intercrop Good ModerateSmall grains Poor PoorFallow land Poor ModerateOrganic strip Good ModerateOrganic crop Poor PoorFarmstead Poor PoorHerbaceous strip Good GoodWoody strip Poor PoorConservation Reserve Program Good GoodHay Moderate ModerateDry prairie Good GoodWet prairie Good ModeratePasture Poor ModerateShrubland Moderate PoorUngrazed forest Poor PoorUngrazed floodplain Poor PoorUngrazed upland forest Poor PoorGrazed upland forest Poor PoorUngrazed savanna Good GoodGrazed savanna Moderate ModerateWetland Poor PoorPond Poor PoorStream Poor PoorEngineered wetland Poor PoorRoad Poor Poor

ifmore thanhalf of thespecies inaguildarecapableofsubstantial (70% of maximum) population expansionfor the land cover type, that land cover type is “good”habitatif more than half of the species in a guild are capableof minimal (10% or less of maximum) population ex-pansion for the land cover type, that land cover typeis “poor” habitatelse the land cover type is “moderate” habitat.

We aggregated from five classes to three classes(good, moderate, poor habitat quality) to make the pre-cision of our habitat typology consistent with widelyaccepted conventions for characterizing habitat by anoncontinuous measure when landscape indices are ap-plied. In many habitat investigations, landscape ecolo-gists have focused on binary landscapes composed onlyof “suitable” and “unsuitable” patch types (Franklinand Forman, 1987; Andren, 1994; Fahrig, 1997; Turneret al., 2001).Turner et al. (2001)state, “Although there

are areas where the probability of suitable habitat ishigh, there are also extensive areas of moderate proba-bility thatmay still serve as habitat patches. Landscapeand population ecologists are only beginning to in-corporate a more continuous representation of habitatsuitability into their conceptual framework” (p. 215).We employed a three-level ordinal habitat typology toretain the sense of valence in the five-classsepm typol-ogy we used for the validity comparison, and, at thesame time, be consistent with the more coarse (bino-mial) scales that have typically been used when land-scape indices are used to characterize habitat patterns.

The cumulative effect of the data conversion proce-dures – vector to raster data, and conversion and ag-gregation of land cover classes – can be substantialfor the interpretation of landscape pattern indices. Forexample, dissolving patch boundaries is not likely tobe as problematic with 87 land cover types as withonly three land cover types. On the other hand, apply-ing landscape indices to 87 land cover types would be


problematic because index values would not be directlyrelevant to the ecosystem property of interest, and hav-ing many values would make interpretations and com-parisons monumentally difficult. Applying some landcover indices to vector data, rather than raster data, isnot possible with existing index software.

4. Results

To evaluate the reliability of the indices we chose,we compared our index values to published values(Table 1). In most cases our resulting index values werewithin the range of published values; in other cases wecould identify data attributes or context issues that con-tributed to out-of-range values. With this comparison,we considered that none of our indices was unreliable.

Where our values fell outside of the range of in-dex values reported elsewhere in the literature (Fig. 3),some of the differences were explained by distinctcharacteristics of Corn Belt landscapes (i.e., highproportion of agriculture, low proportion of wood-

F red bar al numbers)f ss-leve

land classes), differences among classification schemes(each investigator may define land cover classes, suchas woodland, differently, which challenges compar-isons across studies), or the resolution of spatial data(we compared our 3 m resolution results to data reso-lutions ranging from 1 to 200 m). For example, class-level contagion and mean nearest neighbour values(Fig. 3) appear much higher and lower, respectively,than other investigators reported, but these values areexplained, at least in part, by the resolution of spatialdata.McGarigal and Marks (1995), for example, indi-cate that fine-resolution data increases contagion (con-tag) values through an increase of like-adjacencies inpatch-internal cells.

To evaluate the validity of the indices we chose,we tested for correlations among indices of differ-ent attributes of pattern, and consistent correlationswithin a specific attribute of pattern (e.g., functionalconnectivity;Table 5). While we found a consistentlysignificant positive correlation among proportion-of-habitat (pland) and aggregation (ai) indices (measur-ing proportion of habitat and functional connectivity,

ig. 3. Bar chart comparing resulting index values and ranges (or several landscape indices (note: index names are inTable 3; cla

s, vertical numbers) to published index values (boxes, horizontl indices list class type measured).


Table 5Number of significant correlations (P < 0.05) as a fraction of thenumber of comparisons for landscape indices and SEPM results

Landscape index Within landscape Across all With SEPMpattern attribute indices results

Landscape-level indicesMPS 0/1 n/aa 0/1MSIEI 0/1 n/a 1/1RPR 0/1 n/a 0/1IJI 0/1 n/a 0/1

Class-level indicesPLAND 14/30 n/a 1/6MPS 13/30 n/a 1/6AI 13/30 5/18 0/6CONTAG 13/30 5/18 0/6MNN 4/30 0/18 0/6IJI 0/30 0/18 0/6

a n/a: not applied. For some comparisons (e.g., different classifica-tion schemes, different landscapes, different mammal guilds) withinlandscape attributes there were not additional measures of the at-tribute, thus no correlations are calculated.

respectively), there were no correlations between in-terspersion and juxtaposition index (iji) and any of theother functional connectivity indices, or betweeniji andany of the other indices of pattern attributes. Among thefunctional connectivity indices,ai andcontag werefrequently significantly correlated (as we expected –they measure a similar aspect of pattern), but we notedcases where the relationship was inverse (an unusualfinding as the two indices should follow a commondirection and magnitude). Comparing theai andcon-tag values with the other indices and the spatial data,we determined thatcontag was erratic, especially incases where pattern differences between two alterna-tive landscapes were small.

We also evaluated the validity of our chosen in-dices by testing for correlations among index valuesand our colleagues’sepm results for small mammalhabitat consequences (Clark et al., submitted). In mostcases, indices and thesepm were not significantly cor-related (Table 5). We identified significant correlationsfor some ways of operationalizing habitat (e.g., for aparticular guild of small mammals, or for a particulartype of habitat), but none of the correlations was con-sistent across different ways of operationalizing habi-tat (Table 5). That is, while the correlations betweenan index value and the model results could be signif-icant when we operationalized habitat by guilds, the

same index was not significant if we operationalizedhabitat in another way. For planning and design appli-cations, indices should be more robust. Our evaluationsof validity – among indices and with thesepm – did notidentify any indices that could be relied upon for validinferences for all modes of operationalizing mammalhabitat.

5. Conclusions

Our investigation sought to answer the question:are landscape pattern indices an efficient and accu-rate way to compare alternative plans or designs forsmall mammal habitat quality?If indices could reli-ably and validly imply small mammal habitat quality,they might also imply other ecological functions. Theresults of our investigation suggest that although cur-rently available indices appear to be reliable measuresand may usefully document differences in landscapepatterns, they are not consistently valid measures ofsmall mammal habitat quality. To help advance land-scape indices as an applied tool, future studies shoulduse other, additional tests of reliability and internal va-lidity with other data sets at more data resolutions. Ingeneral, our study leads us to conclude that, at thistime, designers and planners should use landscape in-dices only with great intellectual and methodologi-cal care and as one of many measures of landscapeperformance.

nd-s unc-t texta anda re-l gicalf s too ersw om-p planso

5

arec itatp itat.T d ur-

In order for planners or designers to use lacape pattern indices to understand ecological fions, practitioners must review the landscape connd data characteristics that affect index valueslso be aware of the lack of explicitly documented

ationships between landscape indices and ecolounctions. The results of our investigation lead uffer the following advice for planners and designho want to use or critique the use of indices to care the ecological consequences of alternativer designs.

.1. Landscape context of application

The Corn Belt landscapes that we measuredharacterized by few and small high-quality habatches, and large patches of poor-quality habhese characteristics are shared with suburban an


ban landscapes. Landscape pattern indices have notcommonly been applied to these highly fragmentedlandscapes where fine-scale features may be relativelyimportant habitats. When indices have been appliedto such landscapes, coarse spatial resolution obscuredfine-scale pattern variation.

We found that applying landscape pattern indicesto a highly fragmented landscape of fine-scale habitatpatterns does not yield valid information. Two of theindices that we applied (contagandai) were at theirleast effective when proportions of the land cover classmeasured (in this case, “good” habitat) in the land-scape were low and relatively invariant. When land-scape classes are converted and aggregated to makespatial data practical for the application of indices,small linear features that are good habitat can confoundthese pattern indices because of how linear featuresconnect distant and distinct patches. The small fea-tures like fencerows and roadsides that are dispersedthroughout the cultural landscape especially maintainthe relative invariance ofcontag andai, connectinggood habitats such as prairie remnants and riparian veg-etation.

Advice: If the landscape is highly fragmented, theland cover class being measured is low in proportion,and the measured land cover class frequently occursas linear elements, do not rely upon configuration in-dices such as contagion or mean nearest neighbourdistance to provide useful information because theseindices may not be valid in this context. Composi-t ofl at-t es.C alidw apec

5

ffectt ione . Wea ilitya ga-t ningr uesa ions

5.3. Conversion effects

Accurately represented landscapes begin with high-quality data (such as air photos, remotely sensedimages, or mapped points and paths). Representa-tion accuracy can be changed with data conversion(e.g., vector to raster, digitizing analogue data, re-sampling). For example, dissolved boundaries betweencontiguous patches of the same type can coinci-dentally increase the size, change the shape, andchange the spatial relationships among landscapepatches.

Advice: Review data quality – are the data anadequately accurate representation of the landscapecharacteristics of interest? Carefully check spatialdata to determine sources and processing proce-dures (i.e., meta-data) to learn if relevant smallelements are represented in the data (e.g., small el-ements may be eliminated by re-sampling at coarserresolutions). Landscape plans and designs shouldprovide enough detail about the presence and char-acter of each land cover type for incorporation intoa gis for analysis of ecological characteristics ofinterest.

5.4. Classification and aggregation effects

While aggregation of data classes may be neces-sary for manageable use of landscape indices, aggre-gation of land cover classes can compound conversione con-s ndc e tot mallm ov-e mayn

ions an-a l in-f icalf theh or-p ana tor ar-a in-i ugh

ional indices are not confounded by the patternand covers and provide better information about pern variations in small-patch fragmented landscapompositional indices, however, may not be vays to infer ecological consequences of landschange.

.2. Data issues

We observed two types of data procedures that ahe validity of landscape pattern indices: conversffects, and classification and aggregation effectslso observed a data attribute that affected reliabnd validity: spatial data resolution. In our investi

ion these three data issues were critical to obtaieliable, valid index values. However, these issre not always explicitly stated in index applicattudies.

ffects by reducing the number of patch types andequently increasing the likelihood of contiguity. Laovers should be classified with direct relevanche effect on a specific ecological property (e.g., sammal habitat quality). Applying indices to land crs classified for other purposes (e.g., planning)ot be informative.

Advice:Use themost directly relevant classificatcheme describing the critical land cover and mgement attributes. If necessary, collect additionaormation about land cover attributes and ecologunctions to enhance spatial data. For example, ifabitat quality of different patch types is known, incorate that information into plans or designs usingttribute table in agis. If the data are inadequateepresent all ecologically significant land cover chcteristics, the inadequacy could substantially dimsh the usefulness of any index application. Altho


landscape pattern indices may not be as efficient atool if planners and designers must intensively checkgis data for accuracy, planners and designers can im-prove spatial data by articulating highly specific landcover descriptions (e.g., row cropswith or without con-servation tillage) so that alternative plans or designscan be more accurately compared for ecological con-sequences. Planners and designers should base theirapplication of landscape pattern indices on the mostdirectly relevant classification scheme (of the severalavailable).

5.5. Spatial data resolution

Common challenges associated with high-resolution spatial data are slower analyses andincreased digital file sizes. Our investigation has iden-tified an important non-technical challenge associatedwith high-resolution data: fine-scale linear habitatpatches that affect the calculation of landscape patternindices, with misleading results. An ecological inves-tigation may require high-resolution data to representsmall landscape patches, yet if fine-scale elements areapparent, and linear elements are common in the data,configuration indices may be misleading. We foundthat we could not glean much useful informationfrom high-resolution spatial data (mostly becauseof linear features), but neither would we be able topresent useful findings if we had not included all of thee malp Thep det tionw thea ant,fi ingi

re-s t eco-l non( Iffi s tob tiona . Int relym ternd

5.6. Landscape pattern index behaviour

We chose indices that have been reported to be use-ful measures of landscape pattern and represent uniqueaspects of pattern, and that were relevant to the ecolog-ical phenomena of interest (i.e., that affect small mam-mal populations in Corn Belt agricultural landscapes).While previous research has explored the best measuresof landscape pattern (O’Neill et al., 1988; Gustafson,1998; Hargis et al., 1998; O’Neill et al., 1999) welearned that for specific circumstances (e.g., smallmammal population viability, highly fragmented CornBelt landscape context, high-resolution spatial data),indices do not always provide accurate informationabout the ecological consequences of landscape plans.

Advice:Currently, compositional indicesseemmoreuseful for characterizing landscape pattern changes inhighly fragmented contexts. Composition index valuescan assert pattern differences even when data resolu-tion is high or data include connective linear elements.Many indices have been recommended by researchers,but their values can be validly used only to measure apattern difference, not to measure a difference in eco-logical function.

5.7. The future of landscape indices

If landscape pattern indices continue to develop asthey have since 1995 (the advent offragstats), theirusefulness will likely improve. Remotely sensed spa-t olu-t anda eryfi d de-s his isg

ign-i for-m andp hen-s that“ ir-i tedt ly,w boutl f ef-f ,2

cological elements that are relevant to small mamopulations (e.g., roadside or fencerow habitats).roblem is one of efficiency and validity: we conclu

hat while re-sampling data at a coarse resoluould diminish important landscape elements,pplication of landscape pattern indices to relevne-scale data may yield unreliable and misleadndex values.

Advice:Chooseadata resolution adequate to repent the landscapeelements thatmayhave relevanogical effects. Violating the scale of the phenomeeither its extent or resolution) is a critical error.ne-scale linear features are common in the plane evaluated, index values that measure configurare unlikely to provide for meaningful comparisonshis situation the planner or designer is advised toore upon compositional indices to assert a patifference.

ial data are increasingly available at sub-metre resion, supporting landscape classification schemesnalytical tools that are better at distinguishing vne-scale changes in landscapes. For planners anigners who work at both fine and coarse scales, tood news.

Investigators, especially those planning and desng alternative landscapes, require much more in

ation about the relationship between patternsrocesses (Opdam et al., 2001). A recent compreive text for teaching landscape ecology statesthere is a need to build a collective library of empcal studies in which ecological responses are relao particular landscape configurations. Unfortunatee have the power to measure and report more aandscape pattern that we can interpret in terms oects on ecological processes” (p. 108,Turner et al.001).


Future landscape pattern indices should address theproblem areas that we have identified: highly frag-mented contexts, high-resolution data, linear elements,and measurement of landscape configuration. Of great-est future interest will be indices of landscape patternsthat have been shown to be related to ecological func-tions. Only those indices that are shown to relate toecological functions in the target context (both dataand spatial) should be looked upon as useful ways tocompare those ecological consequences of alternativeplans or designs.

6. Summary

While landscape pattern indices appear to have greatpotential for comparing alternative landscape plans fortheir ecological consequences, we found no indices thatvalidly implied small mammal habitat quality. Land-scape pattern indices may usefully quantify patterndifferences, distinct from ecological function, amongsome landscapes. They may be more valid for compar-ing landscapes for which relevant features are apparentat coarse scales. We advise planners and designers to beexceedingly cautious in making ecological inferencesfrom pattern indices for fragmented landscapes withfine-scale habitat features. At present, planners and de-signers should consider indices as tools for comparingpatterns rather than as tools for comparing ecologicalfunctions of alternative landscape plans.

A

EPAp ando notn tionA hipf or-et r-s ankJ nedf inga l-a ea n,

Scott Campbell, Tom Crow, and John Witter for theiradvice and guidance with this research, and M. Els-beth McPhee and two anonymous reviewers for theircomments on drafts of this manuscript.

References

Ahern, J., 1991. Planning for an extensive open space system – link-ing landscape structure and function. Landscape Urban Plan. 21(1–2), 131–145.

Andren, H., 1994. Effects of habitat fragmentation on birds andmammals in landscapes with different proportions of suitablehabitat—a review. Oikos 71 (3), 355–366.

Barrett, G.W., Peles, J.D., 1999. Landscape Ecology of Small Mam-mals. Springer, New York.

Bergin, T.M., Best, L.B., Freemark, K.E., Koeheler, K.J., 2000. Ef-fects of landscape structure on nest predation in roadsides ofa midwestern agroecosystem: a multiscale analysis. LandscapeEcology 15, 131–143.

Bernstein, N.P., 1998. Iowa’s declining flora and fauna: a review ofchanges since 1980 and an outlook for the future. J. Iowa Acad.Sci. 105 (3), 133–140.

Bishop, R.A., Joens, J., Zohrer, J., 1998. Iowa’s wetlands, presentand future with a focus on prairie potholes. J. Iowa Acad. Sci.105 (3), 89–93.

Botequilha Leitao, A., Ahern, J., 2002. Applying landscape eco-logical concepts and metrics in sustainable landscape planning.Landscape Urban Plan. 59 (2), 65–93.

Clark, M.E., Danielson, B.J., Santelmann, M.V., Nassauer, J.I.,White, D., Freemark, K.E., Rustigian, H.L. Modeling mammalcommunities in future landscapes of the American Midwest, sub-mitted.

Coiner, C., Wu, J.J., Polasky, S., 2001. Economic and environmentalreek

C enta-and-

C landmi-

2 (1),

C se ofagri-

C tternsBelt

apeUni-

C rm-r for

D raph-ds.),

cknowledgements

This research has been funded in part by: NSF-roject number R8253335-01-0 (the conclusionspinions are solely those of the authors and areecessarily the views of the Environmental Protecgency); a Natural Systems Agriculture Fellows

rom The Land Institute (Salina, Kansas); USDA Fst Service grant number 98-JV-11231300-034,Cul-ural indicators of ecological quality; and the Univeity of Michigan Rackham Graduate School. We thoe Eilers and Ben Eilers for the digitizing of desigutures, Mark Clark and Brent Danielson for sharnd elaborating theirsepm, Hong He for making avaible hisai software, and Kevin McGarigal for on-linssistance withfragstats. We also thank Dan Brow

implications of alternative landscape designs in the Walnut CWatershed of Iowa. Ecol. Econ. 38 (1), 119–139.

ollinge, S.K., 1996. Ecological consequences of habitat fragmtion: implications for landscape architecture and planning. Lscape Urban Plan. 36 (1), 59–77.

ollinge, S.K., Forman, R.T.T., 1998. A conceptual model ofconversion processes: predictions and evidence from acrolandscape experiment with grassland insects. Oikos 866–84.

olunga-Garcia, M., Gage, S.H., Landis, D.A., 1997. Responan assemblage of Coccinellidae (Coleoptera) to a diversecultural landscape. Environ. Entomol. 26 (4), 797–804.

orry, R.C., Nassauer, J.I., 2002. Managing for small patch pain human-dominated landscapes: cultural factors and Cornagriculture. In: Liu, J., Taylor, W. (Eds.), Integrating LandscEcology into Natural Resource Management. Cambridgeversity Press, Cambridge, MA, pp. 92–113.

ruse, R.M., 1990. Strip intercropping. In: Keeney, D. (Ed.), Faing Systems for Iowa: Seeking Alternatives. Leopold CenteSustainable Agriculture, Ames, IA, pp. 39–41.

anielson, B.J., Anderson, G.S., 1999. Habitat selection in geogically complex landscapes. In: Barrett, G.W., Peles, J.D. (E


Landscape Ecology of Small Mammals. Springer, New York, pp.89–103.

Delong, S.C., Tanner, D., 1996. Managing the pattern of forestharvest: lessons from wildfire. Biodiversity Conserv. 5 (10),1191–1205.

Dunn, C.P., Sharpe, D.M., Guntenspergen, G.R., Stearns, F., Yang,Z., 1990. Methods for analyzing temporal changes in landscapepattern. In: Turner, M.G., Gardner, R.H. (Eds.), QuantitativeMethods in Landscape Ecology, 82. Springer, New York (pp.173–198).

Dunning, J.B., Stewart, D.J., Danielson, B.J., Noon, B.R., Root, T.L.,Lamberson, R.H., Stevens, E.E., 1995. Spatially explicit popu-lation models: current forms and future uses. Ecol. Appl. 5 (1),3–11.

Fahrig, L., 1997. Relative effects of habitat loss and fragmenta-tion on population extinction. J. Wildlife Manage. 61 (3), 603–610.

Farrar, D.R., 1981. Perspectives on Iowa’s declining flora and fauna– a symposium. Proc. Iowa Acad. Sci. 88 (1), 1.

Finder, R.A., Roseberry, J.L., Woolf, A., 1999. Site and landscapeconditions at white-tailed deer vehicle collision locations in Illi-nois. Landscape Urban Plan. 44 (2–3), 77–85.

Franklin, J.F., Forman, R.T.T., 1987. Creating landscape patternsby forest cutting: ecological consequences and principles. Land-scape Ecol. 1 (1), 5–18.

Gustafson, E.J., 1998. Quantifying landscape spatial pattern: what isthe state of the art? Ecosystems 1, 143–156.

Gustafson, E.J., Parker, G.R., 1992. Relationships between landcoverproportion and indices of landscape spatial pattern. LandscapeEcol. 7 (2), 101–110.

Hargis, C.D., Bissonette, J.A., David, J.L., 1998. The be-havior of landscape metrics commonly used in the studyof habitat fragmentation. Landscape Ecol. 13 (3), 167–186.

He, H.S., DeZonia, B.E., Mladenoff, D.J., 2000. An aggregation in-cape

H case. 46,

J g. In:logy.lph,

L re onorerron.

L er-

L pesing

L ingscapelogy,

Mabry, K.E., Barrett, G.W., 2002. Effects of corridors on home rangesizes and interpatch movements of three small mammal species.Landscape Ecol. 17 (7), 629–636.

Marino, P.C., Landis, D.A., 1996. Effect of landscape structureon parasitoid diversity and parasitism in agroecosystems. Ecol.Appl. 6 (1), 276–284.

McGarigal, K., Marks, B.J, 1995. FRAGSTATS: spatial pattern anal-ysis program for quantifying landscape structure. General Tech-nical Report, USDA Forest Service Pacific Northwest ResearchStation. Number: PNW-GTR-351. p. 122.

Nassauer, J.I., 1995. Culture and changing landscape structure. Land-scape Ecol. 10 (4), 229–237.

Nassauer, J.I., 1999. Culture as a means of experimentation and ac-tion. In: Weins, J.A., Moss, M.R. (Eds.), Issues in LandscapeEcology. International Association for Landscape Ecology, Fac-ulty of Environmental Sciences, University of Guelph, Guelph,Ontario, Canada, pp. 129–133.

Nassauer, J.I., Corry, R.C. Agricultural Watersheds Research.http://www.snre.umich.edu/faculty-research/nassauer. Date ofAccess: 18 January 2004.

Nassauer, J.I., Corry, R.C. Using normative scenarios in landscapeecology. Landscape Ecol., in press.

Nassauer, J.I., Corry, R.C., Cruse, R.M., 2002. Alternative futurelandscape scenarios: a means to consider agricultural policy. J.Soil Water Conserv. 57 (2), 44A–53A.

O’Neill, R.V., Hunsaker, C.T., Timmins, S.P., Jackson, B.L., Jones,K.B., Riitters, K.H., Wickham, J.D., 1996. Scale problems inreporting landscape pattern at the regional scale. Landscape Ecol.11 (3), 169–180.

O’Neill, R.V., Krummel, J.R., Gardner, R.H., Sugihara, G., Jack-son, B., DeAngelis, D.L., Milne, B.T., Turner, M.G., Zyg-munt, B., Christensen, S.W., Dale, V.H., Graham, R.L., 1988.Indices of landscape pattern. Landscape Ecol. 1 (3), 153–162.

O’Neill, R.V., Riitters, K.H., Wickham, J.D., Jones, K.B., 1999.osyst.

O weencape

O plant

(2),

P oralHaut-scape

P caperrett,

als.

P tion-n-

P lt Eu-on of

dex (AI) to quantify spatial patterns of landscapes. LandsEcol. 15 (7), 591–601.

ietala-Koivu, R., 1999. Agricultural landscape change: astudy in Ylane, southwest Finland. Landscape Urban Plan103–108.

ongman, R.H.G., 1999. Landscape ecology in land use planninWiens, J.A., Moss, M.R. (Eds.), Issues in Landscape EcoThe International Association for Landscape Ecology, GueOntario, pp. 112–118.

andis, D.A., Haas, M.J., 1992. Influence of landscape structuabundance and within-field distribution of european corn bLepidoptera Pyralidae larval parasitoids in Michigan. EnviEntomol. 21 (2), 409–416.

aurie, M., 1975. An Introduction to Landscape Architecture. Amican Elsevier Publishing Company, New York.

i, X., Lu, L., Cheng, G., Xiao, H., 2001. Quantifying landscastructure of the Heihe River basin, north-west China uFRAGSTATS. J. Arid Environ. 48, 521–535.

udwig, J.A., 1999. Disturbance and landscapes: the little thcount. In: Wiens, J.A., Moss, M.R. (Eds.), Issues in LandsEcology. The International Association for Landscape EcoGuelph, Ontario, pp. 59–63.

Landscape pattern metrics and regional assessment. EcHealth 5 (4), 225–233.

pdam, P., Foppen, R., Vos, C., 2001. Bridging the gap betecology and spatial planning in landscape ecology. LandsEcol. 16 (8), 767–779.

rr, D.B., Pleasants, J.M., 1996. The potential of native prairiespecies to enhance the effectiveness of theOstrinia nubilalispar-asitoidMacrocentrus grandii. J. Kansas Entomol. Soc. 69133–143.

an, D., Domon, G., de Blois, S., Bouchard, A., 1999. Temp(1958–1993) and spatial patterns of land use changes inSaint-Laurent (Quebec, Canada) and their relation to landphysical attributes. Landscape Ecol. 14, 35–52.

eles, J.D., Bowne, D.R., Barrett, G.W., 1999. Influence of landsstructure on movement patterns of small mammals. In: BaG.W., Peles, J.D. (Eds.), Landscape Ecology of Small MammSpringer, New York, pp. 41–62.

enhollow, M.E., Stauffer, D.F., 2000. Large-scale habitat relaships of neotropical migratory birds in Virginia. J. Wildlife Maage. 64 (2), 362–373.

leasants, J.M., Bitzer, R.J., 1999. Aggregation sites for aduropean corn borers (Lepidoptera: Crambidae): a comparis

http://www.snre.umich.edu/faculty-research/nassauer


prairie and non-native vegetation. Environ. Entomol. 28 (4),608–617.

Reed, R.A., Johnson Barnard, J., Baker, W.L., 1996. Fragmentationof a forested Rocky Mountain landscape, 1950–1993. Biol. Con-serv. 75 (3), 267–277.

Robson, C., 1993. Real World Research: A Resource for So-cial Scientists and Practicioner-Researchers. Blackwell Science,Malden, MA.

Rustigian, H.L., Santelmann, M.V., Schumaker, N.H., 2003. Assess-ing the potential impacts of alternative landscape designs on am-phibian population dynamics. Landscape Ecol. 18 (1), 65–81.

Santelmann, M., Freemark, K., White, D., Nassauer, J., Clark, M.,Danielson, B., Eilers, J., Cruse, R., Galatowitsch, S., Polasky, S.,Vache, K., Wu, J., 2001. Applying ecological principles to land-use decision-making in agricultural watersheds. In: Dale, V.H.,Haeuber, R.A. (Eds.), Applying Ecological Principles to LandManagement. Springer-Verlag, New York, pp. 226–252.

Santelmann, M., White, D., Freemark, K., Nassauer, J., Eilers, J.,Vache, K., Danielson, B., Corry, R., Clark, M., Polasky, S., Cruse,R., Sifneos, J., Coiner, C., Wu, J., Debinski, D. Assessing alter-native futures for agriculture in the US Corn Belt. LandscapeEcol., submitted for publication.

Schumaker, N.H., 1996. Using landscape indices to predict habitatconnectivity. Ecology 77 (4), 1210–1225.

Schwartz, M.W. (Ed.), 1997. Conservation in Highly FragmentedLandscapes. New York, Chapman & Hall, 1997.

Schwartz, M.W., van Mantgem, P.J., 1997. The value of small pre-serves in chronically fragmented landscapes. In: Schwartz, M.W.(Ed.), Conservation in Highly Fragmented Landscapes. Chap-man & Hall, New York, pp. 379–394.

Smith, D.D., 1998. Iowa prairie: original extent and loss, preservationand recovery attempts. J. IA Acad. Sci. 105 (3), 94–108.

Thomas, M.B., Wratten, S.D., Sotherton, N.W., 1991. Creation of is-land habitats in farmland to manipulate populations of beneficialarthropods: predator densities and emigration. J. Appl. Ecol. 28,

T of is-ficial

arthropods: predator densities and species composition. J. Appl.Ecol. 29, 524–531.

Thompson, C.M., McGarigal, K., 2002. The influence of researchscale on bald eagle habitat selection along the lower Hud-son River, New York (USA). Landscape Ecol. 17 (6), 569–586.

Tischendorf, L., 2001. Can landscape indices predict ecological pro-cesses consistently? Landscape Ecol. 16 (3), 235–254.

Turner, M.G., 1989. Landscape ecology: the effect of pattern on pro-cess. Ann. Rev. Ecol. Syst. 20, 171–197.

Turner, M.G., Arthaud, G.J., Engstrom, R.T., Hejl, S.J., Liu, J.G.,Loeb, S., McKelvey, K., 1995. Usefulness of spatially explicitpopulation-models in land management. Ecol. Appl. 5 (1), 12–16.

Turner, M.G., Gardner, R.H., O’Neill, R.V., 2001. Landscape Ecol-ogy in Theory and Practice: Pattern and Process. Springer-Verlag,New York.

Turner, M.G., Ruscher, C.L., 1988. Changes in landscape pattern inGeorgia, USA. Landscape Ecol. 1 (4), 241–251.

Vache, K.B., Eilers, J.M., Santelmann, M.V., 2002. Water qualitymodeling of alternative agricultural scenarios in the US corn belt.J. Am. Water Resour. Assoc. 38 (3), 773–787.

Wegner, J., Henein, K., Fahrig, L., 1999. Effects of vegetation typeand adjacent agricultural matrix on fencerow use by small mam-mals: a nonmanipulative experiment. In: Barrett, G.W., Peles,J.D. (Eds.), Landscape Ecology of Small Mammals. Springer,New York, pp. 249–260.

Wiens, J.A., Crist, T.O., With, K.A., Milne, B.T., 1995. Fractal pat-terns of insect movement in microlandscape mosaics. Ecology76 (2), 663–666.

Robert Corry is an assistant professor of landscape architecture,University of Guelph, where he teaches landscape ecology, analysis,and design.

J ar-c ductsr .

906–917.homas, M.B., Wratten, S.D., Sotherton, N.W., 1992. Creation

land habitats in farmland to manipulate populations of bene

oan Iverson Nassauer, FASLA, is a professor of landscapehitecture, University of Michigan, where she teaches and conesearch related to landscape ecology, perception, and design

limitations of using landscape pattern indices to evaluate … · limitations of using landscape...

Documents