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PredictingSpeciesOccurrencesIssues ofAccuracyand Scale
Edited by
J. MICHAEL SCOTT
PATRICIA J. HEGLUND
MICHAEL L. MORRISON
JONATHANB. HAtJFLER
MARTIN G. RAPHAEL
WILLIAMA. WALL
FRED B. SAMSON
ISLANDPRESSWashington" Covelo • London
Copyright © 2002 Island Pt_,_s
All rights reserved uud,'r International and Pan-American (.;opyrighr Con
vcntions, No pazt of this book may be reproduced in any form or by anymeans without permission in writing from the publisher: Island Press, 1718
Connecticut Avenue, N.W., Suite 300, Washingron, DC 20009,
ISI,AND PRF-qS is a trademark of The Center for Resource Economics.
No copyright -claim is made in the work of Brian B. Boroski, ,Loseph D.Clark, Donald 1.. DcAngelis, C. Andrew Dolloff, Jeffrey R. Dunk, MikeGcrtsch, Greg (;oldsmkh, Stevcn J. Harper, Richard S. Holthansen, Can_lyn"1: I lunsaker, Michael A. Huston, Douglas H. Johnson, Krlstine Johns, m,Chirre Ke_klcr, A. Ross Kicstcr, Steven T. Knick, William B. Krohn, William
A: Link, Shcrri L. Miller, Randy Molina, Clinton T. Moore, Barry S. Muldar,
James T. Peterson, C. John Ralph, Martin G. Raphael, Bruce E. Rieman,!.ynn M. Roberts, Margaret J. R6be_t._en, J. Andrew Royle, Fred B. San|son,
GlenA. Sargeant, John R. SaLter, Ann-Marie Shapiro, Theodore R. Simon.%Jane E. Smith, David Solis, Howard B, Stauffer, George N, Steger, David It.li. Stockwell, Glen Taylor, Kathryn "['homas, Bruce C. Thompson, DenisWhite, Brian Woodbtidge, Adrienne Wright, Cynthia J. Zabd, employe_s offederal or state governments.
I.ibrary of Congress Caraloginv-in-Publicat.ion Data
Predicting species occurrences : issues of accuracy and scale edited byJ. Mid, at Scott ... let al.].
p. era.
h_cludes bibliographical references (p.)ISBN 1-55963-787-0 (clnth : alk, papa.r)
1, Zoogeography. 2. Animal populations, 3, Habitat (Ecology) 4.Spatia) eculogy. L Scott, J, Michael.
QLt01 .P64 2001578'.0.9_dc2 I
British Gataloguing-in-Pablicatl,m Data availahle,
I'rinted on recycled, acid-free paper
Manufactured h_the United States of America
'1.0987654.321
Contents
Foreword Theoryand Practicein Australia and
Peter H. Rauen xiii New Zealand
Preface Michael P. Austin 73
Michael E Goodchild xv Chapter 6
Introduction Habitat Models Based on Numerical
J. Michael Scott, Patricia J. Heglund, Comparisons
Michael L. Morrison, Jonathan B. Haufler, K. Shawn Smallwood 83
Martin G. Raphael, William A. Wall, and Chapter 7Fred B. Samson 1
The Roleof Category Definition in Habitat
Introductory Essay: Models: Practical and Logical Limitations of
Critical Issues for ImprovingPredictions Using Boolean, Indexed, Probabilistic, and
Michael A. Huston 7 FuzzyCategories
Kristina E. Hill and Michael W..Binford 97
PART1-Conceptual Framework Chapter 8Use of Regional-scaleExploratory Studies to
Introduction to Part 1: Determine Bird-habitat Relationships
The Conceptual Basis of Species Distribution Jack S. Youngand Richard L. Hutto 107Modeling: Time for a ParadigmShift?
Raymond J. O'Connor 25PART2---Temporaland SpatialScales
Chapter 1
Foundationsof Species-EnvironmentRelations Introduction to Part 2:
Patricia J. Heglund 35 Role of Temporaland Spatial Scale 123
Chapter 2 Michael L. Morrison
StandardTerminology:Towarda Common Chapter 9
Languageto AdvanceEcologicalUnderstanding PredictingDistributionand Abundance:andApplication Thinkingwithin andbetweenScales
Michael L. Morrison and Linnea S. Hall 43 Brian A. Maurer 125
Chapter 3 Chapter 10
Linking Populations and Habitats: Where A Comparison of Fine-and Coarse-resolution
HaveWe Been?Where Are We Going? EnvironmentalVariablesTowardPredictingDean E Stauffer 53 VegetationDistribution in the Mojave Desert
Chapter 4 Kathryn Thomas, Todd Keeler-Wolf, andJanet Franklin :1.33
Approachesto Habitat Modeling:The Tensions
betweenPattern and Process and between Chapter 11
Specificityand Generality The Influenceof Spatial Scale on LandscapeBeatrice Van Home 63 Pattern Description and Wildlife Habitat
Chapter 5 AssessmentMargaret Katherine Trani (Griep) 141CaseStudies of the Use of Environmental
Gradients in Vegetationand Fauna Modeling:
viii Contents
Chapter 12 Chapter 20
Predicting the Occurrence of Amphibians: Modeling Wildlife Distribution within Urbanized
An Assessment of Multiple-scale .Models Environments: An Example of the Eurasian Badger
Catherine M. Johnson, Lucinda B. Johnson, Meles meles L. in Britain
Carl Richards, and Val Beasley 157 Amanda Wright and Alan H. Fielding 255
Chapter 13
Dynamic Patterns of Association between PART3--Modeling Tools and
Environmental Factors and Island Use by AccuracyAssessmentBreeding Seabirds
Introduction to Part 3:Catherine M. Johnson and William B. Krohn 171
Modeling Tools and Accuracy Assessment
Chapter 14 Randall B. Boone and William B. Krohn 265
Geographic Modeling of Temporal Variability in
Habitat Quality of the Yellow-billed Cuckoo on the Chapter 21What Are the Appropriate Characteristics of an
Sacramento River, Miles 196-219, California
Steven E. Greco, Richard E. Plant, and Accuracy Measure?
Reginald H. Barrett 183 Alan H. Fielding 271
Chapter 15 Chapter 22A Minimalist Approach to Mapping Species'
Effects of Spatial Scale on the Predictive Ability ofHabitat: Pearson's Planes of Closest Rt
Habitat Models for the Green Woodpecker in
Switzerland John T. Rotenberry, Steven T. Knick, and
Claudine Tobalske 197 James E. Dunn 281
Chapter 16 Chapter 23
Wildlife Habitat Modeling in an Adaptive GeosPatial Data in Time: Limits and Prospects for
Framework: The Role of Alternative Models Predicting Species Occurrences
Michael ]. Conroy and Clinton T. Moore 205 Geoffrey M. Henebry and James W. Merchant 291
Chapter 17 Chapter 24Predictions and Their Validation: Rare Plants in
Contrasting Determinants of Abundance inthe Central Highlands, Victoria, Australia
Ancestral and Colonized Ranges of an Invasive
Brood Parasite Jane Elitb and Mark Burgman 303
D. Caldwell Hahn and Raymond ]. O'Connor 219 Chapter 25
Chapter 18 Semiquantitative Response Models for Predictingthe Spatial Distribution of Plant Species
Biodiversity Conflict Analysis at Multiple Spatial
Scales Antoine Guisan 315
Christopher B. Cogan 229 Chapter 26
Chapter 19 Patch-based Models to Predict Species Occurrence:Lessons from Salmonid Fishes in Streams
A Collaborative Approach in Adaptive Management
at a Large-landscape Scale Jason B. Dunbam, Bruce E. Rieman, and
Cynthia J. Zabel, Lynn M. Roberts, Barry S. James T. Peterson 327
Mulder, Howard B. Stauffer, Jeffrey R. Dunk, Chapter 27
Kelly Wolcott, David Solis, Mike Gertsch, Brian Autologistic Regression Modeling of American
Woodbridge, Adrienne Wright, Greg Goldsmith, Woodcock Habitat Use with Spatially Dependent Data
and Chirre Keckler 241 David S. Klute, Matthew J. Lovallo, and
Walter M. Tzilkowski 335
Contents ix
Chapter 28 Chapter 36A Neural Network Model for Predicting Northern PredictingVertebrate Occurrences from Species
Bobwhite Abundancein the Rolling Red Plains of Habitat Associations: Improvingthe InterpretationOklahoma of Commission Error Rates
Jeffrey J. Lusk, Fred S. Gutbery, and Sandra M. Schaefer and William B. Krobn 419
Stephen ]. DeMaso 345 Chapter 37
Chapter 29 Assessment of Spatial Autocorrelation inIncorporatingDetection Uncertainty into EmpiricalModels in Ecology
Presence-Absence Surveys for Marbled Murrelet Mary Cablk, Denis White, and A. Ross Kiester 429
Howard B. Stauffer, C. John Ralph, and Chapter 38Sberri L. Miller 357 RankedModeling of Small Mammals
Chapter 30 Based on Capture Data
Accuracyof Bird RangeMaps Based on Habitat Vickie]. Smith and Jonathan A. ]enks 441
Maps and Habitat Relationship Models Chapter 39Barrett A. Garrison and Thomas Lupo 367 Calibration Methodology for an Individual-based,
Chapter 3"1 Spatially Explicit Simulation Model: Case Study ofA Monte Carlo Experimentfor Species Mapping White-tailedDeer in the Florida EvergladesProblems Christine S. Hartless, Ronald E Labisky, and
Daniel W. McKenney, Lisa A. Venier, Kenneth M. Portier 447
Aidan Heerdegen, and Mick A. McCarthy 377
Chapter 32 PARTn PredictingSpeciesMeasuring PredictionUncertainty in Modelsof PresenceandAbundanceSpecies Distribution
Introduction to Part 4:Jennie L. Pearce, Lisa A. Venier, Simon Ferrier,
and Daniel W. McKenney 383 PredictingSpecies Presence and AbundanceFrances C. James and Charles E. McCullocb 461
Chapter 33TowardBetter Atlases: Improving Presence- Chapter 40
absence Information Multimodeling: New Approaches for Linking
Douglas H. Johnson and Glen A. Sargeant 391 Ecological ModelsLouis J. Gross and Donald L. DeAngelis 467
Chapter 34
Predictingthe Distributions of Songbirds in Chapter 41Forests of Central Wisconsin Challengesof Modeling FungalHabitat: When and
Margaret J. Robertsen, Stanley A. Temple, and Where Do You Find Chanterelles?Tina A. Dreisbacb, Jane E. Smith, and
John Coleman 399Randy Molina 475
Chapter 35Chapter 42
PoissonRegression: A Better Approach to
ModelingAbundance Data? Predicting Presence�Absence of Plant Species for
Malcolm "EJones, GeraldJ. Niemi, JoAnn M, Range Mapping: A Case Study from WyomingWalter Fertig and William A. Reiners 483
Hanowski, and Ronald R. Regal 411
x Contents
Chapter 43 Chapter 50
A Model to Predict the Occurrence of Surviving Modeling Bird Abundance from Forest InventoryButternut Trees in the Southern Blue Ridge Data in the Boreal Mixed-woodForests of Canada
Mountains Pierre R. Vernier, Fiona K. A. Schmiegelow, and
Frank T. van Manen, Joseph D. Clark, Scott E. Steve G. Cumming 559
Scblarbaum, Kristine Johnson, and Glenn Taylor 491Chapter 51
Chapter 44 Species Commonnessand the AccuracyofPredicting Meadow Communities and Species Habitat-relationship Models
Occurrences in the Greater Yellowstone Ecosystem Jason W. Karl, Leona K. Svancara, Patricia ].
Diane M. Debinski, Mark E. Jakubauskas, Heglund, Nancy M. Wright, and J. Michael Scott 573Kelly Kindscher, Erika H. Saveraid, and
Chapter 52Maria G. Borgognone 499
Spatial Analysis of Stopover Habitats ofChapter 45 Neotropical Migrant Birds
ModelingSpecies Richness and Habitat Suitability Scott M. Pearsonand Theodore R. Simons 581for Taxaof Conservation Interest
Chapter 53Erica Fleishman, Dennis D. Murphy, and
Effects of Niche Width on the Performance andPer Sjfgren-Gulve 507
Agreementof Avian Habitat Models
Chapter 46 Jeffrey A. Hepinstall, William B. Krohn, and
Discontinuity in Stream-fish Distributions: StevenA. Sader 593Implications for Assessing and PredictingSpecies
Chapter 54OccurrenceA Test and Comparison of Wildlife-habitat
Paul L. Angermeier, Kirk L. Krueger, andModelingTechniques for Predicting Bird .
C. Andrew Dolloff 519Occurrenceat a RegionalScale
Chapter 47 Randy Dettmers, David A. Buehler, and
A GIS-basedHabitat Model for Wood Thrush, John B. Bartlett 607Hylocichla mustelina, in Great Smoky MountainsNational Park Chapter 55
DistributionalPredictionBasedon EcologicalSusan A. Shriner, Theodore R. Simons, and
Niche Modelingof Primary Occurrence DataGeorge L. Farnsworth 529
A. Townsend Peterson, David R. B. Stockwell,
Chapter 48 and Daniel A. Kluza 617ControllingBias in Biodiversity Data
David R. B. Stockwell and Chapter 56Statistical Mappingof Count Survey Data
A. Townsend Peterson 537J. Andrew Royle, William A. Link, and
Chapter 49 John R. Sauer 625ModelingCowbird Occurrences and Parasitism
Chapter 57Rates: Statistical and Individual-based
Influence of Selected Environmental Variables on
Approaches GIS-habitatModels Used for GapAnalysisAnn-Marie Shapiro, Steven J. Harper, and
Carlos Gonzalez-Rebeles, Bruce C. Thompson,James Westervelt 547
and Fred C. Bryant 639
Contents xi
Chapter 58 Chapter 62A Distribution Model for the Eurasian Lynx (Lynx Using a Spatially Explicit Model to AnalyzeEffects
lynx) in the Jura Mountains, Switzerland of Habitat Management on Northern Spotted OwlsFridolin Zimmermann and Urs Breitenmoser 653 Martin G. Raphael and Richard S. Holthausen 701
Chapter 63
PARTS--Predicting Species: Estimating the Effective Area of Habitat Patches
Populationsand Productivity inHeterogeneousLandscapesThomas D. Sisk, Barry Noon, and
Introduction to Part 5: Haydee M. Hampton 713Mappinga Chimera?
Edward O. Carton 663 Chapter 64DemographicMonitoring and the Identification of
Chapter 59 Transients in Mark-recapture ModelsFunctional Definition of LandscapeStructure M. Philip Nott and David E DeSante 72"1Using a Gradient-basedApproach
David M. TheobaM and N. Thompson Hobbs 667PART6--Future Directions
Chapter 60
ModelingHabitat-basedViability from Chapter 65
Organismto Population Predicting Species Occurrences: Progress,
Gary ]. Roloff and Jonathan B. Haufler 673 Problems, and Prospects
Chapter 61 John A. Wiens 739Relations between CanopyCoverand the Literature Cited 751
Occurrenceand Productivity of California Contributors 837
Spotted Owls Index 847Carolyn T..Hunsaker, Brian B. Boroski, and
George N. Steger 687
CHAPTER
34PredictingtheDistributionsofSongbirdsinForestsofCentralWisconsin
Margaret J. Robertsen, Stanley A. Temple, and John Coleman
any studies have demonstrated the importance land cover extracted from a GIS database were used toof fine-grained habitat features, typically map habitat areas in Texas for two endangered song-
within plots the size of a bird's territory, on predicting bird species, the golden-cheeked warbler (Dendroica
the distribution of songbirds (Cody 1985a,b; Block chrysoparia), and the black-capped vireo (Vireo atri-and Brennan 1993). Others have considered the im- capi!lus) (Shaw and Atkinson 1990).
GISs and categorical models can be combined toportance of coarse-scale variables, measured overlandscapes much largerthan a territory or home range quantify habitat use across a landscape. Pereira and(Shriner et al., Chapter 47; Boulinier et al. 1997; Itami (1991) applied a logistic regression model in a
GIS environment to predict the distribution of Mt.Lauga and Joachim 1992; Gustafson and Crow 1994;Pearson 1993). Habitat selection is believed to be a Graham red squirrels based on environmental and lo-
cational variables. Mladenoff et al. (1995) used a sim-multiscale hierarchical phenomenon spanning selec-liar approach to predict the presence or absence oftion of a geographic range (first-order selection) towolf packs as a function of spatial indices and land-
patterns of habitat utilization (third- or fourth-orderselection) (Johnson 1980). Combining fine- and scape variables.
Forest songbirds have been identified as high-prior-coarse-grained levels of habitat selection requires a ity indicators for ecosystem management in the Bara-
multilevel approach, boo Hills of Wisconsin (Baraboo Hills WorkingGeographic information systems (GIS) can be used
Group 1994). The purpose of the study described in
to draw relationships between bird species and envi- this chapter was to apply a multidisciplinary toolronmental variables and map distributions across (GIS) in developing predictive habitat models that
large geographic areas. Palmeirim (1988) used a GIS could be used for the conservation of forest songbirdsto display the predicted distribution of songbirds by across a 580-square-kilometer landscape. Our first ob-
extracting cover-type information from Landsat satel- jective was to determine whether we could reliablylite imagery (Thematic Mapper) and by incorporating predict the distribution of six forest songbird species
rules on patch size requirements. Green et al. (1987) across the landscape by combining a GIS and a cate-
combined bird survey data with Landsat land-cover gorical modeling procedure (logistic regression). As a
information to predict the abundance of hooded war- result we wanted to identify important forest stand
biers across a two-million-hectare area of the Yucatan and landscape-scale habitat components and to pre-Peninsula. Information on slope, aspect, geology, and dict the distribution of songbirds based on these
399
400 PREDICTING SPECIES OCCURRENCES
variables. Our second objective was to demonstrate ventory, (2) songbird data collected during point-
the usefulness of the resulting models for long-term count censuses, and (3) coarser landscape measures
management of the forest landscape by projecting and spatial indices derived from GIS data (1:100,000how bird habitats would change under realistic future scale).scenarios.
Forest-stand Inventory Data
Study Area Data collected by The Nature Conservancy from1991 to 1993 provided information on stand-scale
The Baraboo Hills ecosystem, located in south-central habitat variables that we believed would be impor-Wisconsin, is an area renowned for its biological di- tant in determining the distribution of forest song-
versity. This status was formalized in 1994 when it bird species (Robertsen 1995). Forest stands were de-
was •declared a "bioreserve" by The Nature Censer- fined as relatively homogeneous units of forest with
vancy. Twenty-seven natural communities, seventy- similar plant species composition and structure. Our
seven rare or sensitive plant and animal species, and research took place in "southern forest" communi-135 bird species have been documented within this ties (sensu Curtis 1959) in the Baraboo Hills because
area (Baraboo Hills Working Group 1994). This ex- of the extent and importance of these types on theceptional biodiversity can be partially explained by landscape (Clark et al. 1993a). We chose six forest-high topographic relief and the area's location at the
stand variables to use in model development: domi-• junction of three ecoregions: the Moraines District,
nant tree type, dominant tree size class, dominantthe Driftless District, and the Central Sand Plains Dis-
tree stocking density, understory type, understory
trict (Curtis 1959). Habitats of this ecosystem range size class, and disturbance class (Table 34.1; Robert-from dry ridgetops covered with scrubby oaks and
sen 1995).glade species to cool moist valleys harboring species
such as white pine and northern hemlock. Forest Songbird Data: Selection of PlotsThe Baraboo Hills contains 22,260 hectares of for-
Point counts are an efficient censusing technique whenest surrounded by a largely agricultural landscape.the objective is to identify habitat use across a largeThe shallow, rocky soils of the Baraboo Hills made
much of this area unsuitable for farming. Nearly geographic area (Bibby et al. 1992). A total of 550
6,475 hectares of continuous forest remain in the Hills point-count census plots were established within 233in contrast to other southern forests in the Midwest, southern forest stands to gather information on the
distribution and habitat preference of songbirdswhich have undergone extensive fragmentation. How-
ever, human-related pressures increase daily, and only (Robertsen 1995). In 1993, 468 plots were established
11 percent of the area is currently protected from de- to collect data necessary for the development of mod-
velopment. The forest community types we studied for els. In 1994, eighty-two new plots were censused for
this project are not considered rare on a national level the purpose of validating the predictive model for one
but are highly significant in the context of statewide species, the Acadian flycatcher (Ernpidonax virescens)conservation efforts. In other words, the Baraboo (Robertsen 1995).
Hills is the only remaining large tract of deciduous Census plots were visited once, and censuses vcereforest in south-central Wisconsin. conducted within three hours of sunrise from June 1
to July 2 in 1993 and from June 12 to June 19 in
1994. This period covers the breeding season and the
Methods peak singing hours for male songbirds of most species
To develop our models, we used available GIS data in the Baraboo Hills (Mossman and Lange 1982). The
layers and collected field information on the distribu- number of singing males of each species was recorded
tion of birds. The following sources of data were within a 50-meter radius of the observer during a five-
available: (1) vegetation data from a forest-stand in- minute time period.
34. Predicting the Distributions of Songbirds in Forests of Central Wisconsin 401
TABLE34.1.
Forestsongbirdspeciesof concernandelevenforest-standand landscapevariablesused in the developmentof predictivesongbirdhabitat models.
Forestsongbirds Forest-standvariables Landscape(coarse-scale)variables
Veery Dominanttype: northern hardwoods, Distanceto edge(meters):< 100, 100-200,centralhardwoods,oak > 200
Ovenbird Dominanttree sizeclass (dbh): Distanceto water (meters):<100, 100-200,5-11 in., 11-15 in., 15-24 in. > 200
Chestnut-sidedwarbler Understorytype: centralhardwoods, Percentforest coverwithin 200 meters (%):northern hardwoods < 80, 80-99, 100
Easternwood-pewee Understorysizeclass (dbh):0-5 in., 5-11 in. Slope(%):<10, 10-25, >25
Greatcrestedflycatcher Dominanttree stockingdensity Aspect:north,'east/west, south(sq.ft/acre): 20-50, 51-85, 86+
Acadianflycatcher Disturbanceclass: grazed,unknown
Landscape-scale Data et al. 1992) and if we had detected at least thirty indi-
We expected that the following landscape-scale vari- viduals during our censuses. By choosing this as our
ables might be important in a songbird habitat analyses: cutoff, we made the decision to exclude rare and acci-
distance to edge, percent forest covet; distance to water, dental species to the study area. We were most inter-
slope, and aspect (Table 34.1). These variables were ested in species such as the Acadian flycatcher that are
measured by using the spatial analytical capabilities of still abundant in the Hills but considered less abun-
ArcInfo GIS software. The first step in obtaining these dant or rare on a statewide scale. One or two years of
measures involved creating a point coverage that surveys may be insufficient for sampling specialized or
mapped our bird census plots. A vegetation- rare species, and such species may require specific sur-
community coverage mapped the distribution of forest vey methods (Karl et al., Chapter 51).
and nonforest and allowed us to calculate percent forest We selected eleven habitat variables based on evi-
cover and distance-to-edge measures for each census dence of biological significance and independence
plot. ArcInfo GRID was used to obtain percent forest from other variables using a Pearson correlation
measures, which were grouped into three equal-sized analysis (Table 34.1, Robertsen 1995). Variables that
categories: less than 80 percent cover, 80-99 percent were considered but excluded from analysis based on
cover, and 100 percent cover (Robertsen 1995). A a correlation with another variable of 0.5 or greater
stream data layer (USGS digital line graph data, included community type, understory density, and per-
1:100,000 scale, and USGS 7.5-minute quads, 1:24,000 cent forest cover within 100 and 300 meters of a plot.
scale) was used as the source of information for meas- Songbird and habitat data were noncontinuous and
uring distance to water. Based on a digitized contour required a categorical approach to modeling. We ran a
map of the area, a triangulated irregular network (TIN) univariate analysis on the 1993 census data to gelect
was created to determine slope (less than 10, 10-25, habitat variables for inclusion in models (SAS Institute
greater than 25 percent) and aspect (Robertsen 1995). 1985). For each species, we evaluated the significance
of each of the eleven habitat variables using a proce-
Model Development dure outlined by Hosmer and Lemeshow (1989) and
We began building our models by selecting songbird excluded those with p greater than 0.25 from further
species (dependent variables) and habitat features (in- consideration.
dependent variables). We chose songbird species if In another premodeling step, cross-tabulations
they were of special concern in the region (Thompson were run between all combinations of dependent and
402 PREDICTING SPECIES OCCURRENCES
independent variables to check for marginal zeroes Capen et al. 1986). Karl et al. (Chapter 51) found
and to consider possible interactions between habitat that it is possible in some cases to develop and testvariables. Because zero-cell frequencies may lead to models for more abundant species, with data fromerroneous results in categorical analyses due to the only one or two years. Censuses in 1994 were con-
development of a singular (noninverted) covariance ducted at new plots within the six habitat types in-matrix (SAS Institute 1985), when possible, we com- cluded in the 1993 Acadian flycatcher model. Be-
bined categories where zero cells appeared to be a cause of the large number of censuses required to
problem, statistically test the model, the greatest sampling ef-For each species and its remaining set of habitat fort occurred at the extremes (i.e., sites with highest
variables, we ran a multivariate analysis to derive a and lowest probability of occurrence). We comparedfinal equation. Models were developed using the pro- observations for the Acadian flycatcher in each habi-
cedure CATMOD and followed a step-down method tat class in 1994 to those predicted by the 1993(SAS Institute 1985). We tested the importance of each model.variable removed using a likelihood ratio test (G sta- Models for the other five species were not testedtistic). Final models included no more than two habi- with independent data. However, confidence intervalstat variables due to general sparseness of the data (few on the predicted probability of occurrence in habitat
bird observations/total number of counts) and for ease of highest preference were calculated as an indication
of application in a GIS environment. For these same of model precision and are dependent on sample sizereasons, models considered only main effects, or, in (Table 34.2). Further application of these five models ....other words, no interactions between variables, should be preceded by accuracy testing.
The final model for each species included significant
variables and showed a high probability of goodness Model Application
of fit (Smith and Conners 1986; Hosmer and One advantage of a GIS is its ability to create, display,
Lemeshow 1989). Model adequacy was determined by and analyze the consequences of hypothetical changesconsidering the residual (likelihood ratio) chi-square to current landscape conditions. Using ArcInfo, we
value and associated p value with a lack-of-fit test. created scenarios for a 93-square-kilometer area of the
The smaller the chi-square (and the higher the p) the Baraboo Hills and evaluated the implications of each
better the model fits. Probabilities of a species being scenario on songbird distributions. The scenarios were
present or absent in each habitat class were generated chosen to address likely land-use changes: (1) the com-
(SAS Institute 1985). bined effects of harvesting, succession, and fire man-
agement in the southern dry mesic forest communities,Review and Validation of Models and (2) fragmentation of all forest types due to hous-
To assess each model, we considered whether the final ing development. The assumptions required to create
variables for each songbird made sense based on what future conditions were based on expert opinion and
we knew of the biology of a species (Mossman and existing literature (Clark et al. 1993a).Lange 1982; Robertsen 1995). Evaluations based on Our primary interest was in the future condition
biological intuitiveness are a critical component of of relatively undisturbed mature stands of southern
model validation (Burnham and Anderson 1991; Van dry mesic forest and how changes in these stands
Horne 1991). Using insights gleaned from a literature would affect the songbird community. Southern dryreview, we deduced how songbirds would respond to mesic forests are dominated by a mixture of oaks
stand-level variables based on their response to plot- (Quercus alba and Quercus borealis), basswood
level variables (Robertsen 1995). (Tilia americana) and sugar maple (Acer saccharum).
We evaluated the Acadian flycatcher model using These communities are considered unstable with
an independent data set collected in 1994. Testing a dominance by one species likely to last one genera-model with an independent data set is believed to be tion (Clark et al. 1993a). Due to the economic im-
the best measure of accuracy (Fielding, Chapter 21; portance of oak, intense harvesting could rapidly
34. Predictingthe Distributions of Songbirds in Forests of Central Wisconsin 403
TABLE34.2.
Resultsof categoricalmodelsfor Sixspecies, includingresidualchi-squareP for goodness-of-fittest, significantvariables,habitatof highestpreference,and probabilityof occurrence.
Predictedprobability 95%confidenceResidual Habitatof of occurrencein intervalforthe
chi-square highest habitatof predictedSpecies P Variablesandp-value preference highestpreference probability
Acadianflycatcher 0.88 distanceto edge(0.006) > 200 m from edge, 0.45 0.31-0.58understorytype (0.000) northern hardwoods
Veery 0.12 distanceto edge(0.046) > 200 m from edge, 0.23 0.13-0.37understorytype (0.009) aorthern hardwoods
Ovenbird 0.38 dominanttree density (0.000) standbasal area 86+, 0.64 0.49-0.77understorytype (0.045) northernhardwoods '
Chestnut-sided 0.54 dominanttree size (0.002) 5--11 in.(dbh), 0.34 0.17-0.56warbler dominanttree density(0.000) stand basal area20-50
Easternwood -- dominant tree density(0.019) stand basalarea 86+ 0.50 0.40-0.60
pewee
Greatcrested -- dominanttype (0.030) centralhardwoods 0.17 0:08-0.30
flycatcher
change the future composition and structure of of the Baraboo Hills. Plans are underway to improve
southern dry mesic forests in the Baraboo Hills. the main highway between the Baraboo Hills and
Changes in the forest vegetation will affect habitat Madison, Wisconsin, (a growing metropolitan area)
availability for forest songbirds, less than 50 kilometers away. Easier'commuting and
To simulate a future condition, we changed compo- the appeal of the scenic views from the Hills make
sition and structure of 50 percent of the existing ma- them an attractive place to build new homes. Open-
ture stands by making assumptions about timber har- ings created by housing developments create hard
vesting, succession, and fire. A thirty-year timber edge types and lead to fragmentation of the forested
projection for the state of Wisconsin estimates a 36.6- landscape. We created two development scenarios,
percent reduction in the volume of oak in the state random and clustered, each with total area of new
(Spencer et al. 1988). Assuming a similar loss of oak openings equal to 10 percent of the forest area.
in the Baraboo Hills--in other words, a 40 percent We evaluated impacts on songbirds by calculating
loss), we projected that half the reduction (20 percent) the area of "preferred occupied habitat" remaining
would occur due to harvesting and half (20 per- under each scenario. "Preferred habitat" is defined
cent) through succession. Over thirty years, we further here as the conditions with the highest probability of
projected that 10 percent of the existing stands would occurrence for a given species. We calculated number
retain or gain an oak component due to impacts of of preferred territories by multiplying the area of pre-
controlled burning. This projection was based on the ferred habitat by the probability of occurrence and as-
current interest in using prescribed burns in the area. suming territory size to be I hectare (Temple and Cary
We assumed that dominant tree type, dominant tree 1988).
size , dominant stocking density, and understory type
changed as a result of harvesting, succession, and fire. ResultsFragmentation as a result of housing development
has become a growing threat to the large blocks of Based on regional concern and abundance on our cen-
forest critically important to forest interior songbirds sus plots, six forest songbirds were selected for the
404 PREDICTING SPECIES OCCURRENCES
habitat-model-building process. The Acadian fly- TABLE34,3.
catcher (Empidonax virescens), veery (Catharus Logisticregressioncoefficients (Bi)for the effect offuscescens), chestnut-sided warbler (Dendroica pensyl- understorytype and distanceto edgeon the occurrenceofvanica), eastern wood-pewee (Contopus virens), and Acadianflycatchers(Empidonaxvirescens)
great crested flycatcher (Myiarchus crinitus) are of Variableandvariablelevel 1993 (n = 486) :1.994(n = 82)high regional conservation concern (Thompson et al. Understorytype1992) and were relatively abundant ( more than thirty central hardwoods -1.01 -0.65
individuals detected during our censuses). The oven- Northernhardwoods +1.01 +0.65bird (Seiurus aurocapillus) is of lower regional
concern, but it was chosen for our project because it Distance to edge
was the most frequently recorded species. Lessthan 100 meters -0.75 -7.51Final models for each of the six species contained 100-200 meters +0.06 +3.81
one to two variables (Table 34.2). Stand-scale vari- More than 200 meters : +0.69 +3.70
ables were significant in all developed models. Land-
scape-scale (coarse-scale) variables were significant in
two of the six models: the Acadian flycatcher and the
veery. The equation for each of the final models is of types have increased odds of Acadian flycatcher occur,
the following form: rence of exp(2.02), or 7.5 times over central hard-
ln(P/1 - P) = baseline value + VAR1 effect + woods. Similarly, the odds of finding an Acadian fly-
VAR2 effect +... catcher in an area far from edge (more than 200meters) is exp(1.44), or 4.2 times greater than the
where odds of finding this species close to edge (less than 100
P = Probability that the site is occupied, meters) (Table 34.3; Robertsen 1995). The predicted
Highest probability of occurrence ranged from 0.17 probabilities of occurrence (P) for the final Acadian
to 0.64 depending on the species. Residual chi-square flycatcher model ranged from a low of 0.02 (central
P ranged from 0.12 for the veery to 0.88 for the Aca- hardwood understory, close to edge) to a high of 0.45
dian flycatcher (Table 34.2). Residual chi-square P (northern hardwood understory, far from edge).
could not be calculated for the two models with only We found that the probability of occurrence for
one habitat variable. Acadian flycatchers in each habitat class in 1994
In Table 34.3, an example of a final model for the closely followed what was predicted based on the
Acadian flycatcher is summarized. A difference in co- 1993 model (Table 34.4). A chi-squared analysis indi-
efficients between northern and central hardwoods of cared that differences between the two years were
2.02 indicates that northern hardwood understory nonsignificant but that the test lacked the power re-
TABLE34.4.
Observednumberof 1994 plots with Acadianflycatchers(Empidonaxvirescens)presentand absent for six habitatclassescomparedto predictednumbersbasedon the 1993 model.
Understorytype anddistanceto edge Predictedpresent Observedpresent Predictedabsent Observedabsent
Centralhardwoods,< 100 m from edge 0.4 0 17.6 18Centralhardwoods,100-200 m from edge 0.2 0 3.8 4
Centralhardwoods,> 200 m from edge 1.5 3 15.5 14Northernhardwoods,< 100 m from edge 3.2 0 16.8 20Northernhardwoods,100-200 m from edge 1.6 3 4.4 3
Northernhardwoods,> 200 m from edge 6.8 5 8.2 10
34. Predicting the Distributions of Songbirds in Forests of Central Wisconsin 405
700-
sod. DCurrentConditions
I_FutureScenario5OO
o
._.-j
200.
11111.
0-
Acadian Veery Ovenbird Ch.-slded F- Wo_od Pewee Gr. Crested
Flycatcher Warbler Flycatcher
Rgure 34.1. Predictedchangein numberof territorieswithinpreferredhabitat for six species(Acadianflycatcher[Empidonaxvirescens],veery[Catharusfuscescens],ovenbird[Seiurusaurocapi//us],chestnut-sidedwarbler[Dendroicapensy/vanica],easternwood-pewee[Contopusvirens],greatcrestedflycatcher[Myiarchuscrinitus])afterthirtyyearsof timberharvest,fire, andsucces-sionin the BarabooHillsof Wisconsin.
quired to make a definite conclusion due to small sam- 34.1, Table 34.5). Two of the six increased in abun-
ple size in 1994 (X 2 = 8.78, df = 7, p > 0.25). dance: chestnut-sided warbler (+222 percent) and
great crested flycatcher (+8 percent). The greatest ben-
Model Application efit of this future scenario was realized by the chest-
As a result of our harvesting/succession]fire scenario, nut-sided warbler, a species that currently has much
four of the six species showed a decrease in abundance less preferred habitat across the landscape than the
under projected future conditions: Acadian flycatcher other five. The greatest negative effect (a ]9 percent
(-5 percent), veery (-5 percent), ovenbird (-19 per- loss) occurred for the ovenbird, the species currently
cent), and eastern wood-pewee (-11 percent) (Fig. having the largest amount of "preferred" habitat.
Five of the six songbird species experienced losses
in total number of preferred territories (average 13-TABLE34.5.percent loss) under the random development scenario
Predicted percent change in preferred territories for six forest (Table 34.6). The losses resulting from a clustered de-songbirdspecies after thirty yearsof timber harvest,fire, and•successionin the BarabooHills ofWisconsin. velopment scenario ranged from 4 to 14 percent (aver-
Predictedpercent age 8 percent loss) (Table 34.7). The losses of pre-changein preferred ferred territories greater than 200 meters from an edge
Commonname�Scientific name territories(%) was much higher under a random development sce-
Acadianflycatcher/Empidonaxvirescens -5 nario (average 25 percent loss) than under a clustered
Veery/Catharusfuscescens -5 development scenario (average 11 percent loss) (Ta-
Ovenbird/Seiurusaurocapillus -19 bles 34.6 and 34.7). The Acadian flycatcher and the
Chestnut-sidedwarbler/ veery showed a habitat preference for interior forestDendroicapensylvanica +222
and experienced the greatest loss in territories (TablesEasternwood-pewee/ Contopusvirens -11
34.6 and 34.7). The ovenbird and the eastern wood-Great crestedfiycatcher/Myiarchuscrinitus +8pewee did not show a large percentage loss in total
406 PREDICTING SPECIES OCCURRENCES
TABLE34.6.
Percentreductioninpreferredterritoriesforsixforestsongbirdsfollowinga randomhousingdevelopmentscenario.
Percentchangein Percentchangeinpreferredpreferred territoriesmorethan 200 meters
Commonname name territories(%) fromanedge (%)
Acadianflycatcher/Empidonaxvirescens -27 -27
Veery/Catharusfuscescens -27 -27Ovenbird/Seiurusaurocapillus -5 -30Chestnut-sidedwarbler/Dendroicapensylvanica 0 0
Easternwood-pewee/Contopusvirens -7 -31Greatcrested flycatcher/Myiarchuscrinitus -10 -33
TABLE34.7.
Percentreductioninpreferredterritoriesforsixforestsongbirdsfollowinga clusteredhousingdevelopmentscenario.
Percentchangein Percentchangeinpreferredpreferred territoriesmorethan200 meters
Commonname/Scientific name territories(%) froman edge(%)
Acadianflycatcher/Empidonaxvirescens -14 -14Veery/Catharusfuscescens -14 -14Ovenbird/Seiurusaurocapillus -4 -13
Chestnut-sidedwarbler/Dendroicapensylvanica -4 0Easternwood-pewee/Contopusvirens -5 -12Greatcrestedflycatcher/Myiarchuscrinitus -8 -13
number of preferred territories but did show a large was a significant component in the A_adian flycatcher
decrease in the number of preferred territories more and veery models.
than 200 meters from an edge. On the other hand, forest-stand variables were im-portant predictors of songbird occurrence in all of our
models (Table 34.2). Our forest-stand variables mayDiscussion have contained more useful information on habitat
Several studies have indicated the importance of land- than can be measured by landscape-scale variables.
scape-scale features in determining the distribution of Shriner et al. (Chapter 47) found topographic vari-
birds (Lauga and Joachim 1992; Robbins et al. 1989a; ables to be more important than habitat variables in
Lynch and Whigham 1984). Many of the landscape- predicting songbird distribution but for a largelyundisturbed landscape. Ongoing timber harvesting
scale (coarse grain) measures we expected would beand other developments in the Baraboo Hills may re-
important were not statistically significant in any of duce the effectiveness of topographic variables in pre-the models. These included slope, aspect, distance to dicting vegetation types. Understory type and do-mi-
water, and percent forest cover. However, significance nant tree-stocking density factored into more models
tests on data may mask "preference" of resources due than other forest-stand variables. Understory type was
to the subjectiveness of the researcher in defining better at differentiating bird habitats than either dom-
available habitat (Johnson 1980). Although they are inant tree species or distance to water. Dominant tree
similar measures, distance to edge was a better predic- size was a variable of significance for the chestnut-
tot than percent forest cover within 200 meters and sided warbler--a species that utilizes early succes-
34. Predicting the Distributions of Songbirds in Forests of Central Wisconsin 407
_ Sections/'Streams
_Roadsam PreferredACFLHabitat
Forest
N
+
0 1 2 Milesli II I
Figure 34.2. Preferredhabitat for the Acadianflycatcher(EmpJdonaxvirescens) under a random devetopment scenario.
sional stands. This variable was also highly significant coarser-grain measures such as elevation could be
for the ovenbird, which prefers closed-canopy, mature obtained more objectively. In addition, forest stand in-
forests. Our results support the notion that it is ira- ventory data is dynamic by nature and corresponding
portant to consider macro- as well as micro-scale GIS layers must be updated on a regular basis. Be-
habitat features (Block and Brennan 1993). However, cause of the time and resources required to obtain and
researchers should not neglect the importance of maintain landscape-level resource data, it is critical to
stand-level features in favor of coarser landscape-scale evaluate the ability of a shared database to meet mul-
measures, especially in disturbed areas, tiple objectives (Donovan et al. 1987). We were un-
The ability to develop predictive models for species able to develop a model for the wood thrush (Hyloci-
that have a strong association with stand or plot-scale chla mustelina), possibly because this species prefers
variables may be limited by the cost of collecting such gaps within a closed canopy, which was not a forest-
data and by the resolution of GIS layers. Collecting stand measure available in our database. If different
forest stand inventory data for the Baraboo Hills was songbird habitat measures had been collected during
an extensive effort and required skill and experience the forest-stand inventory, they might have improved
on the part of the field crew. Observer bias may have the predictability of models or permitted development
affected the accuracy of this data layer whereas of models for additional species.
i t """I
i i
N
w_ r............-_SectionsE L_..__,Streams
s " RoadsPreferred ACFL Habitat
0 1 2 Miles_ Forest
Rgure 34.3. Preferred habitat for the Acadian flycatcher (Empidonax virescens) under a clustered development scenario.
34. Predicting the Distributions of Songbirds in Forests of Central Wisconsin 409
Modeling Approach and Accuracy One might expect the best models to be developed
We applied an empirical modeling approach based on with species that are habitat specialists yet are abun-the results of 550 songbird censuses. Habitat capabil- dant enough to provide adequate sample size. Our
ity models (a theoretical approach) produce an index ability to predict the occurrence of a species using this
to habitat suitability ranging from zero (unsuitable)to method did not appear to be closely tied to abun-
one (highly suitable). It is difficult to know whether dance, with the exception _ofvery rare species. The
these indices reflect environmental conditions or pop- Acadian flycatcher and chesmut-sided warbler were
ulation response (Morrison et al. 1992). Our ap- detected on less than 12 percent of our census plots
proach directly measures and, quantifies population re- yet produced the best models. The ovenbird was de-
sponse (probability of occurrence) within a given tected on 38 percent of our plots, but the final modelhabitat type. has a lower p value (Table 34.2). This lack of a close
The final models met our expectations with regard relationship between abundance and our ability to
to habitat selection by each species, but the goodness- model preference may be tied to the scale of our study
of-fit test varied from poor for the veery to good for area. The accuracy of model predictions generally ira-
the Acadian flycatcher (Table 34.2). As might be ex- proves at very coarse scales, in other words, the state
pected, the two species with the highest goodness of fit of Wisconsin versus the Baraboo Hills (Boone and
are both habitat specialists, the Acadian flycatcher and Krohn 1999). In addition, all of the six species we de-the chestnut-sided warbler. The Acadian flycatcher's veloped models for could be classified as "abundant"
preference for closed-canopy, southern-mesic forest as compared to those we chose to exclude (thosestands (Mossman and Lange 1982) is revealed by the species present on less than thirty stops). Five of the
importance of the northern understory variable. The six models require additional tests to determine their
chestnut-sided warbler's association with early succes- true accuracy since accuracy is largely independent of
sional habitats is revealed by the importance of tree goodness of fit (Fielding, chapter 21).
size and density in that model. On the other hand, the As would be expected (Karl et al., Chapter 51), we
veery is considered a habitat generalist with regard to were unable to develop models for several species offorest structure, which may explain the relatively poor high concern that occurred in very low densities, such
fit of that model. Dettmers and Bart (1999) found this as the mourning warbler (Oporornis pbiladelpbia)
same pattern in their development of GIS models for and hooded warbler (Wilsonia citrina), due to a high
songbirds, number of zero occurrences in some habitat types. InFor all six species modeled, probability of occur- addition, our methodology would not work well for
rence in the most preferred habitat was relatively low species with a low level of detectability since it
(Table 34.2). Improved measures of habitat may in- is based on identifying bird presence in preferred
crease these probabilities. Karl et al. (Chapter 51) habitats.
found that models developed for heterogeneous land- Predictive models should not be used to forecast
scapes (like the Baraboo Hills) required higher-resolu- into the future until they have been validated against
tion habitat data. More likely, this pattern is a result an independent data set (Morrison et al. 1992). The
of factors other than habitat playing a role in the dis- Acadian flycatcher model is the only model that we
tribution of a species, such as competition, predation, tested with an independent data set. Results of this
stochastic events, weather, and regional population test indicate that the 1994 observations were not sig-
status. Others have found that predictive habitat- nificantly different from the 1993 model predictions,
based models often only account for half the variation but the test lacked the power to make a definite con-
observed in species abundance or density (Morrison et clusion (Robertsen 1995). Better models could possi-
al. 1992). The advantage to our approach is that with bly be derived with increased sample size (Dettmers
our 550 censuses we directly measured this variability and Bart 1999). The performance of these models is
rather than assuming that all "preferred habitat" was unlikely to be as high as the performance of models at
occupied, using coarser grain-habitat features over larger areas
1410 PREDICTING SPECIES OCCURRENCES
(Karl et al., Chapter 51; Fielding, Chapter 21). How- catcher and the veery models contained a landscape-
ever, these models are still useful for land managers in scale (coarse-scale) measure, distance to edge, as a sig-the Baraboo Hills by identifying potential habitats for nificant predictor. We applied these models to look at
protection. Models are unlikely to perform at 100 per- the future impacts of timber harvesting, forest succes-
cent, and their usefulness should be judged based on sion, prescribed burning, and housing developments.
the desired management application and scale (Van Under our timber harvest/succession/prescribed-burn
Horne, Chapter 4; Fielding, Chapter 21). scenario, preferred habitat increased for two of the six
species, the chestnut-sided warbler and the greatManagement Applications crested flycatcher but decreased for the Acadian fly-
The fire/harvesting/succession and housing develop- catcher, veery, ovenbird, and eastern wood-pewee.
ment scenarios demonstrate how these models can be Under a development scenario, random housing devel-
applied by conservationists. If harvesting and succes- opment resulted in a 13 percent average decrease in
sional trends continue, the chestnut-sided warbler will abundance as compared to an 8 percent average de-
benefit the most, whereas the ovenbird will be the crease for clustered housing development. These aver-
most negatively affected (Fig. 34.1). Most timber har- age losses were greater for forest interior habitats: 25
vesting in the Baraboo Hills takes place in central percent for random development and 11 percent forhardwoods stands; however, those stands with an un- clustered development. Based on a test of the Acadian
derstory of northern hardwoods have the highest flycatcher model in 1994, the models we developedhabitat value for the ovenbird, Acadian flycatcher, and using 1993 data looked promising.
veery. Impacts to habitat can be reduced in such cases
by directing timber harvesting to stands with a centralAcknowledgments
hardwoods understory or applying selection harvest-
ing methods. Selection versus clearcut logging would This project was funded by the North Central Re-also benefit the eastern wood-pewee by retaining more search Station, USDA Forest Service, St. Paul, Minn.
canopy cover. The great crested flycatcher showed a We thank the following individuals for their support
small increase in number of territories correlated with of the project and editing of this manuscript, espe-the succession of oak stands. The chestnut-sided war- cially Tom Nicholls, Richard Buech, Kay Burcar,
bier may benefit from created openings, but nesting Mark Nelson, Leanne Egeland, and Steve Robertsen.success in these areas needs to be examined. Cluster- We also wish to acknowledge the University of Wis-
ing housing development helped reduce loss in territo- consin, Madison, Department of Wildlife Ecology andries for most species, especially for the Acadian fly- Land Information Computer Graphics Facility forcatcher and veery,which were significantly more likely technical advice. Other agencies that supported this
to occur far from an edge (Figs. 34.2, 34.3). project either through advice or funding include TheNature Conservancy of Wisconsin, the Wisconsin De-
Conclusion partment of Natural Resources, The Zoological Soci-ety of Milwaukee County, The USDI Fish and Wildlife
We looked at the current and future distribution of six Service, and the Wisconsin Society for Ornithology.
forest songbirds of conservation concern across a re- The USDA Forest Service, Tongass National Forest,
gionally important landscape. All models included for- Wrangell Ranger District in Alaska provided supportest-stand variables, thus emphasizing the importance in the final phases of this project for which we are
of habitat measures at this scale. Only the Acadian fly- grateful.
