Assessment of Habitat Values for Indicator Species and Avian Communities in a Riparian Forest
Dwight Barry, Richard A. Fischer, Karl W. Hoffman, Tami Barry, Earl G. Zimmerman, and Kenneth L. Dickson
Southeastern Naturalist, Volume 5, Number 2 (2006): 295–310
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2006 SOUTHEASTERN NATURALIST 5(2):295–310
Assessment of Habitat Values for Indicator Species and
Avian Communities in a Riparian Forest
Dwight Barry1,5,*, Richard A. Fischer2, Karl W. Hoffman3, Tami Barry3,
Earl G. Zimmerman4, and Kenneth L. Dickson3
Abstract - Habitat suitability index (HSI) models, which are meant to analyze the
habitat value of an area for a particular species of interest, are commonly used to
evaluate species, communities, and habitats for management or impact assessment.
However, many assessments are conducted under strict time and money limitations,
and this use of HSI may lead to inappropriate interpretations. The purpose of our
work was to evaluate the relationships between three HSI models, the presence of
their attendant species, and related avian communities at common assessment time
scales. We compared the habitat values of a north Texas bottomland forest measured
by HSI models for three species commonly used as indicators of this habitat
type, Hairy Woodpecker, Pileated Woodpecker, and Barred Owl, with (1) the actual
presence of each species, and (2) the presence of a larger forest-dependent avian
community. HSI values did not correlate well with either occurrences of the indicator
species or communities for any of the three models. Our results suggest that
these models are not necessarily effective indicators of actual habitat use by indicator
species, and may not be useful in helping managers make decisions that may
affect an entire community related by habitat type. Positive assessments of habitat
value through HSI values should be appraised carefully before management decisions
are initiated, perhaps through outside review and/or a clear discussion of the
context. The primary value of HSI use may be the ability to measure changes in the
habitats themselves over time, and not exclusively as representations of species or
Management strategies and assessments on conservation lands are often
predicated on the assumption that indicator species and their habitat requirements
can be used effectively to represent the current or potential
distribution of a wider array of species. If this is the case, there should be a
strong correlation between the results of habitat analyses for appropriate
indicator species, and the presence of a larger suite of native species,
guilds, and/or communities of management interest. We have observed that
habitat assessments for indicator species are commonly used as a surrogate
for extensive floral and faunal surveys in the applied environmental
1Lewisville Wildlife Management Area, PO Box 310559, Denton, TX 76203. 2US
Army Engineer Research and Development Center, Environmental Laboratory,
ATTN: CEERD-EE-E, 3909 Halls Ferry Road, Vicksburg, MS 39180. 3Institute of
Applied Sciences, University of North Texas, PO Box 310559, Denton, TX 76203.
4Department of Biology, University of North Texas, PO Box 305220, Denton, TX
76203.5Current address - Peninsula College, 1502 East Lauridsen Boulevard, Port
Angeles, WA 98362. Corresponding author - email@example.com.
296 Southeastern Naturalist Vol. 5, No. 2
assessment field. In our experience, these assumptions are often taken for
granted and have not been extensively tested in contexts specific to management
and assessment. Furthermore, due to funding, time, or other
administrative constraints, many potential violations in the interpretation
and application of these methods can occur, leading to poorly-grounded
One use of indicator species for management and impact-assessment
work is the Habitat Evaluation Procedure (HEP), developed by the US Fish
and Wildlife Service (USFWS) to quantify habitat variables important to a
given target species or set of target species chosen to represent the larger
faunal communities of which they are a part (USFWS 1980a, 1980b, 1981).
The target species’ life-history requirements are quantified in a habitat
suitability index (HSI) model that is specific to each species and its primary
habitat(s), and sometimes specific to a particular region. Theoretical and
empirical support for the HSI framework comes from ecological theories
related to habitat selection, niche partitioning, and limiting factors. Studies
abound in the literature that have evaluated, critiqued, and/or supported the
validity of many of these assumptions (e.g., Hall 1988, Harris and Kangas
1988, Hobbs and Hanley 1990, O’Neil and Carey 1986, Roloff and
The HSI models use various habitat metrics (e.g., number of trees ≥ 51cm
dbh/0.4 ha) to evaluate particular sites for habitat suitability for a given
species. Each model provides a numerical index of habitat suitability on a
0.0 to 1.0 scale, based on the assumption that there is a positive relationship
between the index and habitat “carrying capacity” (USFWS 1981). The HSI
model is then used to predict the habitat suitability of the site for that
particular animal. HSI values are combined with the total acreage of a given
cover type to calculate habitat units (HUs) within the assessment areas. HSI
values from several species’ models may also be averaged for a given cover
type. The use of HEP and HSI models is common in habitat and impact
assessment for management in the southeast (e.g., Institute of Applied
Sciences 1995), including in politically contentious developments in bottomland
hardwood (BLH) forests.
Unfortunately, HSI models are often used in practice as implicit indicators
for entire communities, such as the use of a forest-dependent bird
species model to represent all forest-dependent birds in the area. The potential
result of this misapplication is that, given a high habitat-value number
for a particular habitat type, a manager with little-to-no background in
wildlife management could assume that the habitat is good for most or even
all of the animals in that habitat. While this is unfortunate, it is also quite
common for decision makers to have little background in ecology and
sampling methods and no practical contact with the data collectors. This is
often the case when sampling and reporting is contracted out of the agency,
and, as a result, there can be a significant disconnect between field work and
its results and eventual description of management alternatives.
2006 D. Barry et al. 297
Much of the BLH ecosystem in Texas is becoming increasingly fragmented
by urban development, and rapid habitat assessments for impact
assessment, mitigation, and conservation are commonplace. Bottomland and
riparian forests, especially in north Texas, provide food and shelter for an
impressive array of bird species (Barry et al. 2000, Conner and Dickson 1997,
Hoffman 2001, Kellison and Young 1997). Riparian forests are typically a
small part of any landscape, but they are essential habitat for many species of
birds. For example, riparian areas in the western United States make up less
than one percent of the total landscape, but are used by more species of
breeding birds than any other habitat type in North America (Fischer and
Fischenich 2000, Knopf et al. 1988). Unfortunately, the many species of
breeding birds that inhabit BLH forest interiors, such as Coccyzus americanus
L. (Yellow-billed Cuckoo), Seiurus motacilla Vieillot (Louisiana Waterthrush),
and Protonotaria citrea Boddaert (Prothonotary Warbler), have been
declining over the past several decades as their habitats become increasingly
fragmented (Conner and Dickson 1997, Sauer et al. 1999).
There is a need to investigate the relationship between indicator habitat
values and birds in riparian forest ecosystems. These investigations are
critical to determining if indicator species (as represented by their habitat
models) are as effective tools for the conservation and management of these
forests as managers often assume. We explored the HSI index values of a
BLH forest in north Texas and the relationships between these values and
the avian community. Specifically, our purpose was to explore whether HSI
models work for their intended targets when applied under typical management
conditions in the southeast, and whether or not HSI values obtained
from indicator-species’ models can reflect the presence of other forest species
in a particular field situation.
Field Site Description
The Ray Roberts Greenbelt Wildlife Management Area comprises nearly
2000 ha near the center of Denton County, TX. Approximately 500 ha of the
area contain remnant stands and connecting corridors of BLH along the Elm
Fork of the Trinity River (Fig.1). The remaining 1500 ha are regenerating
forests, old fields, and savannah scattered among the remnant forest patches.
The vegetation of the riparian forest is dominated by Ulmus crassifolia Nutt.
(cedar elm), Celtis laevigata Willd. and C. reticulata Torr. (hackberry), and
Fraxinus pennsylvanica Marsh. (green ash), with occasional occurrences of
Quercus macrocarpa Michx. (bur oak), Q. shumardii Buckl. (Shumard oak),
Carya illinoensis (Wangenh.) K. Koch (pecan), and Populus deltoides Bartr.
ex Marsh. (eastern cottonwood) (Barry and Kroll 1999, 2003; Barry et al.
2000). This elm-ash-hackberry type is recognized as a mature stage in many
southeastern bottomland hardwood forests (Hodges 1997). The understory is
a mixture of Smilax rotundifolia L. (common greenbrier), Toxicodendron
radicans (L.) Kuntze (poison ivy), Symphoricarpos orbiculatus Moench
(coralberry), and Elymus virginicus L. (Virginia wild rye). The surrounding
298 Southeastern Naturalist Vol. 5, No. 2
landscape matrix above the bottomland is predominantly rangeland and
agriculture, with a few scattered areas of rural development.
Denton County occupies approximately 2450 km2 in north-central Texas,
just north of the Dallas/Ft. Worth metroplex. Throughout the county, soil
type is the key factor explaining native vegetation distribution (Bailey 1995,
USDA 1980). The climate is humid subtropical, with hot summers and mild
winters (mean temperatures of 4–10 °C in winter and 27–32 °C in summer),
with moderate rainfall of 890 mm per year and periodic drought (Bailey
1995, NWS 2005).
Figure 1. The Ray Roberts
Greenbelt WMA Forest
and sampling locations.
2006 D. Barry et al. 299
We established 62 permanent sampling stations along a transect placed
within the middle of the riparian forest, roughly following the channel of the
Elm Fork. Sampling stations were placed approximately 250 meters apart to
avoid double counting individual birds (Hamel et al. 1996, Ralph et al.
1995). The stations were marked on a map, and then relocated in the field
each season using a GPS unit and aerial photographs.
To test the value of HSI model results, we conducted point counts of
birds four times per year in 1999 and 2000 with sampling periods during
winter, spring and fall migrations, and the summer breeding season. Within
each sampling season, all points were surveyed within a 2-week period, and
surveyors were trained to standardize experience level. We sampled each
point once during each of the eight sampling seasons to simulate the routine
practice in management and impact assessment of sampling a location once,
based on when the funding becomes available or the directive to sample is
given. The order of visitation to all points was determined randomly within
Surveys were conducted as 10-minute, 50-m radius point counts (Hamel
et al. 1996, Ralph et al. 1995). Sampling began at first light and proceeded
for approximately four hours, which allowed for potential detection of some
nocturnal (such as owls) as well as most diurnal species. The 50-m radius
was part of a nested sampling design that recorded approximate distance and
direction of all birds seen or heard within three distance rings of 25-m, 50-m,
and beyond 50-m (Hamel et al. 1996). Mirroring constraints associated with
typical assessments, we did not collect data in a manner that would allow for
adjustment of abundance estimates by detection probabilities. For purposes
of data analysis, we excluded any birds that were recorded outside of the 50-
m radius, as we wished to focus on birds likely to be using specific habitat at
each point count station.
We classified species detected based on whether or not they depend upon a
large amount of forest for their life history requirements (Ehrlich et al. 1988,
Robbins et al. 1989, Sauer et al. 1999, Stokes and Stokes 1996, Whitcomb et al.
1981, and the personal experience of the authors). Forest birds (Table 1) were
chosen for this analysis due to their sensitivity to fragmentation, their general
population declines across the range of BLH, their status as species of management
and conservation concern for several governmental and nongovernmental
organizations, and their potential to serve as charismatic or “flagship” conservation
species in efforts to protect, manage, or restore riparian forests (Conner
and Dickson 1997, NABCI 2000).
The main focus of the study was to match habitat values as measured by
HSI models at particular locations with any birds that were encountered in
these same locations, as these values are sometimes used in managerial
300 Southeastern Naturalist Vol. 5, No. 2
practice as surrogates for entire animal communities. Thus, we evaluated
each permanent sampling station using the methods described in the HSI
models for Picoides villosus L. (Hairy Woodpecker [HAWO]; Sousa
1987), Dryocopus pileatus L. (Pileated Woodpecker [PIWO]; Schroeder
1983), and Strix varia Barton (Barred Owl [BDOW]; Allen 1987)
(Table 2). HSI values were then calculated for each plot using the formulas
described in each model.
These three species were chosen because they are regional residents that
can serve as both indicator and flagship species, HSI models for each are
widely available, these models have been used in the region, and because
suitable spatial and physical habitat for each species, according to the
qualitative descriptions in the models, exists within the Greenbelt. While
each of our indicator species have large home ranges and could have been
passing through a given sampling site, we assumed that because they were in
the site, it was being used for some aspect of their life history. Although
Table 1. Forest birds of the Ray Roberts Greenbelt detected during the study.
Common name Scientific name
Cooper’s Hawk Accipiter cooperii Bonaparte
Red-shouldered Hawk Buteo lineatus Gmelin
Yellow-billed Cuckoo Coccyzus americanus L.
Barred Owl Strix varia Barton
Ruby-throated Hummingbird Archilochus colubris L.
Red-bellied Woodpecker Melanerpes carolinus L.
Yellow-bellied Sapsucker Sphyrapicus varius L.
Downy Woodpecker Picoides pubescens L.
Hairy Woodpecker Picoides villosus L.
Pileated Woodpecker Dryocopus pileatus L.
Eastern Wood Pewee Contopus virens L.
Great-crested Flycatcher Myiarchus crinitus L.
Red-eyed Vireo Vireo olivaceus L.
Warbling Vireo Vireo gilvus Vieillot
Gray-cheeked Thrush Catharus minimus Lafresnaye
Hermit Thrush Catharus guttatus Pallas
Brown Creeper Certhia americana Bonaparte
Winter Wren Troglodytes troglodytes L.
Blue-gray Gnatcatcher Polioptila caerulea L.
Ruby-crowned Kinglet Regulus calendula L.
Golden-crowned Kinglet Regulus satrapa Lichtenstein
Carolina Chickadee Poecile carolinensis Audubon
Tufted Titmouse Baeolophus bicolor L.
White-breasted Nuthatch Sitta carolinensis Latham
Tennessee Warbler Vermivora peregrine Wilson, A.
Northern Parula Parula americana L.
Yellow-rumped Warbler Dendroica coronata L.
Black-and-white Warbler Mniotilta varia L.
Prothonotary Warbler Protonotaria citrea Boddaert
Ovenbird Seiurus aurocapilla L.
Canada Warbler Wilsonia Canadensis L.
Summer Tanager Piranga rubra L.
Rose-breasted Grosbeak Pheucticus ludovicianus L.
2006 D. Barry et al. 301
there are a wide variety of potential problems associated with this assumption
(Garshelis 2000, Hall 1988), in our experience, this is a routine
approach to rapid assessment in this region.
We categorized the forest class of the sampling stations as forest patch or
corridor by delineating the forested area of the Ray Roberts Greenbelt in a
GIS through onscreen digitizing of 1-m resolution USGS Denton East and
Green Valley digital orthophoto data. Once the forest extent within the study
area was delineated, all forest that was within 100 m of non-forest habitat
(i.e., edge forest) was removed from the GIS layer, leaving polygons of
interior forest. The 100-m distance was chosen because edge effects that
cause microclimatic variation or ecological edge effects are minimized or
absent at this distance (Cadenasso and Pickett 2001, McGarigal and
McComb 1995, Oliver and Larson 1990). Sampling stations that fell within
these remaining interior forest polygons were considered to be patch forest,
while stations that did not occur in forest interior were considered to be
occurring in the corridors of forest connecting remnant patches.
The full data set from the 62 point count stations was subdivided by
forest class, with 43 stations occurring in corridors and 19 stations in patch
forests. We calculated the total, mean, and cumulative species richness and
abundance for individual sampling periods, each season (years pooled),
year (seasons pooled), and overall (all data pooled). Also, the HSI model
species were subdivided out of the main data set and compared with HSI
values for each year (all seasons pooled), both years (all data pooled),
season (both years pooled), and each season individually. In our results, we
report both means and medians for our data, as mean HSI values (“averages”)
are most often reported in applied work, while medians are what
some of our statistical analyses evaluate; the differences between the two
are important and instructive.
Table 2. Description of habitat metrics for the habitat suitability index models employed in
Species Plot size Metrics
Hairy Woodpecker 0.4 ha Number of snags > 25-cm dbh/ha
(HAWO) Mean dbh of overstory trees
Percent canopy cover of overstory trees
Percent canopy closure of pines (optional)
Barred Owl 0.4 ha Number of trees ≥ 51-cm dbh
(BDOW) Mean dbh of overstory trees
Percent canopy cover of overstory trees
Pileated Woodpecker 0.4 ha Number of trees ≥ 51-cm dbh
(PIWO) Percent canopy cover of overstory trees
Number of tree stumps > 0.3 m in height
and > 18 cm in diameter
Number of logs > 18 cm in diameter
Number of snags > 38-cm dbh
Mean dbh of snags > 38 cm
302 Southeastern Naturalist Vol. 5, No. 2
Using Statistica 5.5 software (Statsoft 1999), we analyzed our data at an
alpha level of 0.1, as we were most interested in identifying patterns worthy
of further study. The HSI values were not normally distributed, so differences
in HSI values between the presence/absence of model species and
patch/corridor locations were evaluated using the Mann-Whitney U Test.
Chi-square analysis was used to determine if each indicator species occurred
in either patch or corridor forests more often than would be expected by
chance. Spearman’s Rank Order Correlation was used to explore relationships
between the HSI values and avian metrics as outlined above. Any
comparisons that were non-significant were dropped from further analysis.
In addition, the HSI values for each model were stratified by 75th and 25th
percentiles, and any station with an HSI value in the 75th percentile was
considered the best habitat in the Greenbelt relative to overall HSI habitat
values for each model. Bayesian analysis allows for an estimation of the
confidence one might have in the use of the models in this particular
situation; using Excel 2000 software (Microsoft 2000), stratified HSI data
were analyzed with actual occurrence of model species using Bayes’ theorem
and data-based prior distributions (see Anderson  for an example
of this approach). This was done to determine the probability that model
species will actually occur at a given station, given the observation of higher
quality habitat from HSI data.
Habitat suitability index values
The results of the habitat suitability index (HSI) analysis indicated that
suitable habitat in varying quantities occurred for each species within the
Greenbelt. The HSI values for the HAWO model ranged from 0 to 0.95, with
a mean value of 0.65 (median = 0.75). Sites with moderate HSI values were
distributed heterogeneously throughout the forest, with a near-optimal maximum
of 0.95 occurring at 11 stations in both patch and corridor forests. The
minimum of 0.0 occurred at six stations in both patch and corridor forests.
There was no significant difference in habitat value between patch (mean =
0.67, median = 0.85) and corridor (mean = 0.65, median = 0.70) forests for
the HAWO model (U = 352, p = 0.38).
For the BDOW model, HSI values ranged from 0 to 1, with a mean value
of 0.64 (median = 0.77). There was a significant difference (U = 273.5, p =
0.04) between BDOW corridor HSI values (mean = 0.69, median = 0.77) and
patch values (mean = 0.59, median = 0.67). The mean dbh index value for
the BDOW model was higher in corridors than patches, which influenced the
overall result. However, the 0.1 difference in average (and median) habitat
value (HSI) between patches and corridors may not be biologically meaningful;
indeed, the Barred Owl did not seem to show a preference for either
forest class (see below).
For the PIWO model, HSI values ranged from 0 to 0.85, with a mean
value of 0.1 (median = 0). There was no significant difference (U = 681, p =
2006 D. Barry et al. 303
0.21) between HSI values of the corridor (mean = 0.07, median = 0.0) and
patch (mean = 0.18, median = 0.0) forests. Most PIWO HSI values were low;
43 of the 62 stations sampled had a value of 0.0.
Comparisons of HSI habitat values to model species occurrence
Over the two-year study, HAWO was detected 47 times (at 39 of the 62
sampling stations), with most detections occurring in the fall seasons of both
years. The HAWO did not occur significantly more often in patch forests
(n = 29) than in corridor forests (n = 10) based on availability (χ2 = 1.16,
df = 1, p = 0.29). Median HSI value at stations at which HAWO occurred
was 0.8 (n = 39, mean = 0.7, sd = 0.26), whereas stations where it did not
occur had a median HSI value of 0.65 (n = 23, mean = 0.57, sd = 0.34).
However, this difference was not significant (U = 341, p = 0.11).
The BDOW was detected 36 times (at 29 stations), and it also did not
show any significant differences in occurrence based upon availability of
forest class (χ2 = 2.09, df = 1, p = 0.15). Stations at which BDOW was
present or absent had median HSI values of 0.77 (n = 29, mean = 0.68, sd =
0.31) and 0.67 (n = 33, mean = 0.64, sd = 0.29), respectively, a difference
that was not significant (U = 427.5, p = 0.47).
The PIWO was detected 26 times (at 18 stations) in two years, with most
detections occurring during the summer. PIWO detections were concentrated
in and around the largest patches of forest in the Greenbelt. It is
unknown if the birds at these locations represent breeding pairs or individuals.
The occurrence of the PIWO in this forest is the first confirmation of
their presence in the area since 1986 (K. Steigman, McKinney, TX, pers.
comm.). Unlike the other two indicator species, the PIWO occurred significantly
more often in patch forests than would be expected by chance (χ2 =
6.50, df = 1, p < 0.011). The median HSI value was 0.0 for sites with (n = 18,
mean = 0.09, sd = 0.14) and without (n = 44, mean = 0.11, sd = 0.22) PIWO
presence, a non-significant difference (U = 357, p = 0.46) (Table 3).
Bayesian analysis revealed that none of the HSI models satisfactorily
predicted actual occurrence of model species in the study area, given the
prior distribution based on good habitat (i.e., top quartiles of HSI value by
model). The HAWO model performed the best, showing a probability of
0.667 that the higher quality habitat in the Greenbelt (by its HSI value)
would have at least one HAWO. Analysis of BDOW data exhibited a
Table 3. Summary HSI values by model species presence/absence (n refers to number of stations).
n Median (95% CI) Mean (± 95% CI) Range
HAWO HSI Presence 39 0.80 (0.71–0.85) 0.70 (0.09) 0.00–0.95
Absence 23 0.65 (0.50–0.85) 0.57 (0.15) 0.00–0.95
BDOW HSI Presence 29 0.77 (0.67–0.88) 0.68 (0.12) 0.00–1.00
Absence 33 0.67 (0.59–0.84) 0.64 (0.10) 0.03–1.00
PIWO HSI Presence 18 0.00 (0.00–0.19) 0.09 (0.07) 0.00–0.42
Absence 44 0.00 (0.00–0.00) 0.11 (0.07) 0.00–0.85
304 Southeastern Naturalist Vol. 5, No. 2
probability of 0.5 that the BDOW actually occurred at a station considered
by the HSI model to be the better habitat within the Greenbelt. Analysis of
the PIWO model showed a probability of 0.375 that the better habitat as
shown by the PIWO HSI value would have a PIWO present.
Comparisons of HSI values to avian communities
Of the 109 species of birds detected during the designated sampling period
over the 2 years of this study, 33 species were considered forest-dependent
birds (Table 1). For the whole sampling period, a cumulative average of 31
species occurred at each station, with a mean of 12 forest-dependent species
per point. The presence or absence of the indicator species (HAWO, PIWO,
BDOW) did not show a relationship with forest-bird species richness, either in
patch or corridor forest (Fig. 2).
Only the HAWO HSI values had any significant correlations with forestbird
species richness (Table 4). No significant correlations occurred with
species richness and either the PIWO or the BDOW HSI values, and no
Table 4. Significant correlations between HAWO HSI values and forest avian community metrics.
Avian community metric Spearman’s R p
Species richness (fall 1998) 0.26 0.045
Species richness (spring 1999) 0.29 0.022
Species richness (summer 1999) 0.30 0.018
Species richness (winter 2000) 0.29 0.027
Species richness (year 1 average) 0.36 0.004
Species richness (overall average) 0.36 0.004
Species richness (year 1 cumulative) 0.36 0.004
Species richness (overall cumulative) 0.38 0.002
Species richness (year 1 total) 0.36 0.004
Species richness (overall total) 0.32 0.012
Figure 2. Presence of HSI model species compared with cumulative counts of forestbird
species richness for the study duration.
2006 D. Barry et al. 305
significant correlations occurred between the abundance of any forest bird
and HSI habitat values for any of the three model species. An analysis of
potential correlations between HSI model values and measures of the forestbird
community showed 10 significant but weak correlations between the
HAWO model and forest-bird species richness; the highest correlation
among these was for the HAWO model and cumulative forest-bird species
richness (R = 0.38, p = 0.002). Most correlations occurred with data from the
first year of sampling (Fall 1998–Summer 1999); only one correlation occurred
with second year (Fall 1999–Summer 2000) data: HAWO HSI and
forest-bird species richness in Winter 2000 (R = 0.29, p = 0.027). Since we
found a total of only 10 correlations out of 102 data-set comparisons made
that were significant when analyzed at an α = 0.1 level, and since when using
an α = 0.1 threshold, 10 of every 100 correlations found could be due to
chance alone, the degree of statistical correlation is weak and there is a
strong likelihood that it does not represent biological significance.
Our evaluation suggests that these HSI models, as routinely used in the
region, may not provide a basis to justify the assumed link between habitat
value and the presence of indicator species and other habitat-related fauna.
Thus, they may not be helpful as indicators of actual habitat use or presence/
absence by the species. The few, weak correlations between HSI values and
the forest-bird community did not occur any more frequently than might be
expected by chance alone, nor were the correlation values high enough to
suggest biological relationships among habitat values and the forest-bird
community. The results summarized in Tables 3 and 4 suggest that correlations
could arise at any point in time regardless of what relationships might
actually exist between birds and their habitat. Taken together, the models did
not distinguish between areas with and without detections any better than at
random. Bayesian analysis provided the same implications, that we can
assign little confidence to the use of these models in this forest. Thus, if
persons conducting an impact-assessment project had chosen to use these
models in a particular season or year, they might detect a set of relationships
between the birds and the forest habitat that would not necessarily be the
same in any other season or year. As a whole, these results suggest that the
use of HSI models as a surrogate for indicator species and/or avian community
presence is inappropriate given the sampling conditions.
The results of our habitat sampling suggested that little differences exist
between designated corridors and patches, and that it may be possible to
maintain much of the habitat value present in corridors that connect patches.
There is empirical evidence that these corridors are important for a variety of
animals that use them to migrate, disperse, or otherwise move between and
among habitats throughout the landscape (e.g., Beier and Noss 1998,
Machtans et al. 1996). While our studies have shown that similar habitat
values can be present in larger patches as well as within the corridors that
306 Southeastern Naturalist Vol. 5, No. 2
connect them (Barry et al. 2000), the relationships among animals and their
habitats and landscape contexts must be more fully understood and evaluated
before managers should feel encouraged by a positive assessment of
habitat value. Because corridors are often seen as one of the most important
conservation tools in fragmented landscapes (Beier and Noss 1998), this
could be seen as an important finding. However, additional research on
actual patterns of animal use and movement through any conservation corridor
is needed to determine whether corridor habitat, especially as defined by
HSI models, is truly functional. Indeed, the results of this study indicate that
habitat evaluation for indicator species using the HSI format may not provide
the support these evaluations have often been assumed to have.
Although habitat factors, such as structural diversity, are often presented
in the literature and in “common knowledge” as strongly related to
bird diversity (e.g., Flather et al. 1992, MacArthur and MacArthur 1961),
recent research suggests that spatial heterogeneity is a central causal factor
of faunal diversity in ecosystems, as the landscape mosaic itself often
explains more of the variation in diversity of fauna than within-patch
factors such as site-specific habitat characteristics (Franklin 1993, Haas
1995, Henderson et al. 1985, Keitt et al. 1997, Machtans et al. 1996,
Pearson 1993, Pickett and Cadenasso 1995, Robbins et al. 1989, St. Clair et
al. 1998, Storch 2002, Whitcomb et al. 1981). Other studies of the avian
and mammal communities occurring on the Ray Roberts Greenbelt support
these views (Barry et al. 2000, Hoffman 2001). Taken as a whole, these
studies strongly suggest that the landscape may impose important topdown
constraints on avian response to habitat-level factors. Because many
animal species react to landscape factors and other spatial considerations,
the improvement of HSI models will have to include explicit analyses of
landscape context (e.g., Larson et al. 2003).
The use of HSI models has one extremely useful feature: they provide
numerical values that represent habitat conditions in a given place. These
values can be measured from year to year or longer, and can provide a useful
analysis of trends and changes in habitat features. So, while they may not
necessarily have any direct relationship with their target species or a larger
community in the form which most of them take (i.e., habitat-feature based),
they can provide important, more or less objective standards by which
changes (e.g., those wrought by impacts) can be measured directly. Because
the use of numbers is often a considerable part of decision making, this is a
particularly valuable use of the models without having to make claims
regarding the relationships between habitat and wildlife.
Our study was not designed to determine whether HSI models are inherently
flawed or if a broader selection of models is needed; rather, we suggest
that the untailored use of HSI models for the generation of management
alternatives (e.g., the NEPA process) is a serious problem, one that is too
often overlooked by managers who may not have the knowledge to judge
whether or not a study using these models was done appropriately. Our study
2006 D. Barry et al. 307
also suggests that management and impact assessments evaluated under
typical applied-data collection conditions (esp. short and/or inappropriate
time frames) should be carefully appraised to determine their true worth.
This can be accomplished most beneficially through a peer review assessment
of the intended application of HSI values within both initial scope of
work proposals and draft/final reports. This latter type of review should be
conducted before offering the results and (potentially flawed) alternative
management strategies to the public for scoping. If the use of HSI models
cannot accurately reflect wildlife/habitat relationships at a given site, managers
must be honest that their use is only for analysis of changes in habitat
features, and does not necessarily reflect potential wildlife impacts.
Thanks to K. Steigman and M. Guilfoyle for many hours of field work, and to
S. Mabey and four anonymous reviewers who substantially increased the quality of
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