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On Vertebrate Generations and Plant Succession: Incorporating Vegetation Structural Attributes into Vertebrate Modeling
FRANCISCO J. VILELLA
AND
RICHARD B. MINNIS
USGS/BRD Mississippi Cooperative Fish and Wildlife Research Unit,
Mississippi State University, Mississippi State
Species-Habitat Concept and Gap
Analysis
The concept of habitat is understood, even by the lay public, as the
place where an animal resides. As biologists we recognize compo-
nents of habitat (i.e., cover, food) contained within this “place.”
MacArthur and MacArthur (1961) argued an ornithologist could
determine which species were likely to occur in a given location
based on the following criteria: 1) geographic location of habitat,
2) type and structure of habitat, 3) knowledge of geographic range.
Similarly, Udvardy (1969) stated “the mapping of vegetation serves
as the basis of correlation for animal distributions . . . (and)
indicate(s) possible occurrences of animals where they have not
been studied or have been overlooked.” Finally, Thomas (1979)
claimed “plant communities and their seral stages . . . are ecologi-
cally important as niches for wildlife species. The niches are a
product of the plant community, its seral stages, and other environ-
mental factors—including soil type, moisture regime, microclimate,
slope, aspect, elevation, and temperature.” It is important to specify
this description refers to the Grinnellian concept of ecological niche,
which focuses on factors determining the distribution and abun-
dance of species (Grinnell 1917). This autecological approach to
the concept of niche is the basis for developing the habitat models
used by the Gap Analysis Program (GAP).
GAP assesses terrestrial vertebrate biodiversity by mapping the pre-
dicted distribution of terrestrial vertebrates in a given region. GAP
is designed to use the association of wildlife with vegetation and/or
other physical attributes to examine the potential distribution of
vertebrate species and associate it with the existing distribution of
lands being managed for biodiversity conservation. Data devel-
oped by GAP provide decision support for planning the protection
of all terrestrial vertebrate species within a given region. GAP data
sets are probably some of the broadest and most useful for this type
of analysis. But, by assuming that a species occupies all suitable
vegetation types within its range and that all suitable types have
been included as habitat, GAP predictions will always tend to over-
estimate the species distributions (Smith and Catanzaro 1996).
While overestimating the distribution of common species may be
acceptable in some cases and is preferable to consistently underes-
timating distributions (Edwards et al. 1995), it may represent a prob-
lem for rare or patchily distributed species (Smith and Catanzaro
1996). Therefore, vegetation type is not the only factor influencing
use by species; other environmental variables are usually incorpo-
rated to further refine a species distribution (Csuti 1996). Such
factors can be either ecological or physical and may include eleva-
tion, soil type, rainfall, slope, aspect, and patch or polygon size
(see Csuti and Crist [1996] for a thorough explanation of the verte-
brate modeling process of GAP).
Refinement of Models
GAP has significantly contributed to the knowledge and capabili-
ties of natural resource conservation through the use of spatial in-
formation technologies (Prendergast et al. 1998, Schwartz 1999).
As GAP incorporates new software, hardware, and techniques, the
original scope of GAP can be expanded. The last 10 years have
seen a growth of knowledge and enhancement of techniques unpar-
alleled in the past. This expansion of knowledge has forced GAP
to continually revise and upgrade the standards for data products.
For example, where once the minimum mapping unit for land cover
mapping was 100 ha, today it is only 2 ha. Many states are retain-
ing and using the 30 m pixel resolution. Similarly, where once we
obtained a handful of cover classes that often included many forest
alliances, we see the development of detailed techniques such as
decision-rule algorithms (McKerrow 1997) for selection of detailed
individual alliances or ecological complexes from the same satel-
lite data used five years earlier.
Many GAP projects have been incorporating various factors to re-
fine species distributions. For example, Allen et al. (in press) dem-
onstrated the impact of incorporating dispersal distances on overall
species richness in southern Florida. Mattson (1996) demonstrated
the impacts of human presence and access to the potential distribu-
tion of grizzly bear (
Ursus arctos
) habitat in Idaho.
GAP is viewed as a long-term planning tool; however, there has
been some acceptance of the need to continually reevaluate areas
because of a rapidly changing landscape and the impact human dis-
turbance has on biodiversity. This being said, we ask: are GAP
distributions long-term estimates or a snapshot in time? While Csuti
(1996) stresses the importance of GAP distribution maps as long-
term planning tools, he emphasizes the need for information man-
agement systems that allow users the ability to update this informa-
tion.
While GAP is not designed to deal with the short-term need of threat-
ened and endangered species, the examination of these species pro-
vides insight into past events that may have contributed to driving
these species near extinction. Unfortunately, however, most GAP
projects do not examine these species in detail. Smith and Catanzaro
(1996) suggested that performing an analysis of the Red-cockaded
Woodpecker (
Picoides borealis
, RCW) in Arkansas would be use-
less because of the rapidly changing range of the species. This
seems to contradict Csuti (1996) who suggested that an analysis for
a rapidly expanding species such as the Indigo Bunting (
Passerina
cyanea
) would provide useful insight into the temporal dynamics
of this species.
While each GAP state has their individual focus on how to improve
vertebrate models, with each state learning from the success or lack
of success of other states, Mississippi GAP decided to focus on the
impacts of vegetation structure on wildlife habitat distributions. The
state of Mississippi is a leader in timber production. The state has
nearly 7 million hectares of timber land (44% of the total land base),
of which on average 100,000 ha are harvested annually (Missis-
sippi Forestry Commission, unpub. data). These figures highlight
the importance of understanding the seral-stage dynamics for wood-
land species such as RCW and Northern Bobwhite (
Colinus
virginianus
) and how timber harvest may impact habitat availabil-
ity for these species. The Spatial Information Technologies Labo-
ratory in the College of Forest Resources at Mississippi State Uni-
versity developed the land cover map for Mississippi GAP (Batten
1998). From the onset of the project, we determined structural com-
ponents of vegetation would be emphasized in land cover mapping.
A pilot study of a single TM satellite data scene revealed the ability
to distinguish five structural classes of pine (Batten 1998). These
classes included the following: high-density pine, low-density pine,
medium-density pine, recent harvest areas, and recent revegetated
harvest areas. Loblolly pine (
Pinus taeda
) comprised nearly all the
pine classes in this scene, thus all pine alliances were grouped for
this analysis.
These classes, in turn, correspond rather closely to pine forest seral
cycles in Mississippi. Recent harvest areas refer to the stage of
mature forest removal and up to the third year of pine regrowth.
Revegetated harvest areas occur in the time period when pine es-
tablishes itself in the system but has not completely closed the
canopy (usually 3-5 years after regrowth). High-density pine re-
lates to the period of dense canopy cover during the sapling to pole-
timber stage. Canopy cover is 100%, and little understory vegeta-
tion exists. As most pine in Mississippi is managed for timber, it is
usually thinned at around 15 cm (6 in.) DBH. The period from
post-thinning to final thinning relates to medium-density pine. Low-
density pine corresponds to the final stages of pine development
before harvest. As would be expected, there is some degree of con-
fusion between the low- and medium-density pine classes. Even
with this confusion, the product accuracy for these classes aver-
aged 70% and the users accuracy nearly 80%.
Seral Stages and Species Associations
Stated explicitly by Thomas (1979) and implicitly by MacArthur
and MacArthur (1961) and Udvardy (1969) are the potential effects
that vegetation structure and seral stages exert on the presence or
absence of a species. Many examples exist of species (e.g., checker-
spot butterfly) that coevolved with or adapted to specific seral stages
of particular vegetation types (Wahlberg et al. 1996). The Red-
cockaded Woodpecker is a native, nonmigratory species endemic
to mature, pyric pine communities of the southeastern United States.
The distribution of the RCW has contracted, and populations have
declined precipitously throughout much of its range. Population
declines have been attributed to conversion of mature pine stands
to young plantations, hardwood midstory invasion, habitat fragmen-
tation, and demographic isolation (Ligon 1971). The distribution
of RCW is dependent on the extent of mature longleaf (
Pinus
palustris
), loblolly (
P. taeda
), shortleaf (
P. echinata
), and slash (
P.
elliottii
) pine forests in the southeast. Hooper et al. (1980) give a
detailed description of the life history and habitat requirements of
the RCW. Because the RCW is an endangered species in Missis-
sippi and the Southeast, a great deal of ecological information has
been collected on this species in recent years. Detailed information
has also been collected on habitats and species associated with RCW
(Engstrom 1993).
These data can be extremely useful in expanding our understand-
ing of the ecology of not only RCW but other species that rely on
the mature, pyric pine community as well. Bachman’s Sparrow
(
Aimophila aestivalis
), a threatened species, nests in the wiregrass
tussocks of the understory. The endangered gopher tortoise
(
Gopherus polyphemus
) is a keystone species for more than 300
species of invertebrates and 65 species of vertebrates that use the
underground burrows they provide (Dodd 1995). In addition to
these species of concern, an additional 36 mammalian and 86 avian
species have been documented as relying on this ecosystem
(Engstrom 1993). These species include southeastern pocket go-
pher (
Geomys pinetis
), southeastern fox squirrel (
Sciurus niger
),
and Brown-headed Nuthatch (
Sitta pusilla
) (Engstrom 1993).
Effect of Vegetation Structural Information
on Vertebrate Models
If the structural information for pine classes were applied to the
distribution model of RCW in Mississippi, we can reduce the po-
tential overestimation of currently available pine habitat by 940,000
ha simply by eliminating recently harvested pine areas and dense
young stands. Similarly, if medium-density pine proves to be un-
suitable habitat for these species as well, we can reduce the avail-
able habitat statewide by an additional 1.7 million ha (Figure 1).
This revised estimation of currently available habitat can have
sig-
nificant ramifications for reserve design and planning. Although
GAP is designed as a tool to identify unprotected regions of high
biodiversity, we anticipate planning new reserves for the protection
of threatened and endangered species. We feel GAP can and should
provide the best available information for designation of these re-
serves as well.
Figure 1. Estimation of available mature pine habitat for Red-
cockaded Woodpeckers varies greatly depending on the classification
system used. Classifying all suitable pine alliances into a single class
(A) reveals 3.1 million hectares of habitat. Conversely, classification
based on structural (i.e., age) classes (B) indicates 0.4 million hectares
of habitat are currently available. Analysis developed from 30 m
Thematic Mapper data circa 1992.
The last decades have seen a rapid expansion of the scientific basis
for selection and design of nature reserves. Some of this has
cen-
tered on the elucidation of conservation filters and the ability of
nature reserves to capture different levels of biodiversity.
Conser-
vation at both organizational scales (i.e., coarse-filter vs. fine-fil-
ter) has been successful, and both deserve continued support (Shafer
1995). Reserves are designed to provide protected habitat for spe-
cies and communities to maintain their long-term existence. Many
reserves are designed to ensure population persistence for a limited
number of flagship or umbrella species. While GAP is mainly con-
cerned with animal and plant distributions and not abundance, the
issue of vertebrate abundance cannot be totally ignored (Krohn
1996). Population viability should be one of the long-term objec-
tives when planning nature reserves. During the past 30 years, stud-
ies of biogeography, particularly of island biotas, have shown habi-
tat quantity is as fundamental to the survival of a species as is habi-
tat type and quality (Shaffer 1996). In other words, having the proper
type of habitat, even of high quality, may not assure species sur-
vival unless there is enough of it. Overestimation of available habi-
tat for a species dependent on particular seral stages of a given veg-
etation type could mean the difference between species survival or
extinction.
We provide an example using Noxubee National Wildlife Refuge
(NWR), located in east-central Mississippi. Four major vegetation
types dominate the area: hardwood bottomlands, hardwood uplands,
pine, and pine/hardwood. In addition to waterfowl, endangered
species play a major role in management of forest ecosystems at
Noxubee NWR. Intensive management of RCW habitat is a major
focus in mature pine and pine/hardwood stands (Richardson and
Stockie 1995). Thinning and prescribed burning are regularly uti-
lized in areas occupied by RCW. Let us assume Noxubee NWR
has been given the resources to acquire 30,000 ha adjacent to the
refuge to enhance quantity and distribution of old-growth pine habi-
tat. If a primary concern is immediate enhancement of RCW habi-
tat while maximizing its long-term availability, an analysis of GAP
data on all sides of the refuge reveals some interesting results.
Noxubee NWR is located in fairly dense pine habitat extending
west and south of the refuge. An examination of a 30,000 ha block
on either side of the refuge yields slightly different results in terms
of currently available habitat. To the west of the refuge, a 30,000
ha block would acquire 14,327 ha of pine of which 1,656 ha (11.5%
of the total pine) would be suitable or nearly suitable habitat. Con-
versely, the block to the south would yield 18,829 ha of pine with
3,162 ha (16.8%) in an older state of succession. While the total
area of loblolly pine was only slightly different, there was twice as
much existing habitat in the south block than in the west block.
Therefore, by incorporating vegetation structure into the decision
process, refuge personnel can look at both current and future avail-
able habitat.
Conclusion
The GAP approach has been, perhaps unfairly, criticized for at-
tempting to collect species-occurrence data at coarse spatiotempo-
ral scales for use as surrogates of community and ecosystem repre-
sentation and persistence (Conroy and Noon 1996). The continual
development of new reserve selection algorithms will result in more
practical and realistic applications for sustainable protection regimes
(Pimm and Lawton 1998). As GAP matures and spatial informa-
tion technology advances, incorporation of factors such as patch
size, dispersal distances (Allen et al. in press), and structure or age
classes of vegetation will become vital to differentiate between cur-
rently existing habitat and potential habitat. As conservationists
call for greater emphasis on management for biodiversity, it is in-
creasingly important to develop tools for assessing the effects of a
specific management strategy on a wide variety of organisms over
a range of scales, both spatially and temporally. While GAP data
have already proven themselves as a long-term management and
planning tool (Prendergast et al. 1998), we believe the future of gap
analysis lies far beyond a mere complement to single-species man-
agement.
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