This file was created by scanning the printed publication.
Errors identified by the software have been corrected;
however, some errors may remain.
Landscape Ecology and Ecosystem
Management
Dean L. Urban 1
Abstract - Landscape ecology is an interdisciplinary field that embraces
spatial heterogeneity and pattern in ecosystems. Of several key concepts
in ecosystem management, landscape ecology has much to say about
scaling issues and "the natural range of variability" as this applies to the
dynamics of landscape pattern. Over a sufficiently large area, dynamic
habitat pattern-a consequence of biotic processes, environmental
constraints~ and disturbances-exhibits a scaled equilibrium over an area
that is sufficiently large to maintain a constant distribution of habitats of all
types and ages. This area that incorporates the full range of landscape
variability for habitats and their resident meta populations is the "unit
pattern," and to maintain this pattern is the ideal goal of ecosystem
management. Simulation studies suggest that this fanciful ideal will rarely
be met in real systems, but these studies can provide useful predictions of
the natural range of variability one might expect for a system, given the
scaling parameters of its disturbance regime and successional dynamics.
This approach can be extended to incorporate explicit spatial considerations,
environmental gradients, and more realistic ecological details. Meeting this
challenge will require the integration of landscape models into research and
management. Uncertainty in dealing with landscapes from an ecosystems
perspective calls for creative research using "experiments" provided by
management activities, coupled with aggressive efforts to educate ourselves
and the public about this changing perspective.
INTRODUCTION
Here I address the question, What does landscape ecology
offor to sustainable ecosystem management? This is a natural
Landscape ecology is a rapidly evolving field that crosses a
bewildering spectrum of disciplinaty boundaries. Although the
field is still defining itself (Wiens 1992), its hallmark as a
discipline is its focus on spatial heterogeneity and pattern (Risser
et al. 1984, Utban et al. 1987, Thmer 1989, Turner and Gardner
1991). Specifically, landscape ecology is concerned with (1)
detecting and characterizing pattern; (2) explaining how pattern
develops; (3) discovering its implications to populations,
communities, and ecosystems; and (4) describing how pattern
changes through time. As in other fields, there is a healthy
interaction between those interested in more academic or
theoretical issues in landscape ecology, and those driven by more
practical issues related to land management.
question, as the goals of ecosystem management (Behan 1990,
Kessler et al. 1992) overlap substantially with the principle
concerns of landscape ecology. There is a special resonance on
scaling issues and in characterizing the natural range of
variability in large-scale systems. I focus here on vegetation
pattern on ,landscapes, but most of my arguments could be
extended readily to animal metapopulations in habitat mosaics.
Landscape ecology offers no simple recipe for managing
ecosystems; yet, it does offer some useful insights as to how
we might approach this task. Three general insights provide an
outline to the remainder of this paper:
(1) An ideal approach to sustainable landscape
management aims to preserve landscape pattern as a
stationary distribution of patch types. This ideal is
not likely to be met except in simple systems.
1 Forest Sciences Department, Colorado State University, Fort
Col/ins, CO 80523
127
pattern as summarized by spatial statistics (autocorrelation and
power spectra). In very simple cases, this inference wOIked quite
well. But when they introduced a range of distuIbance patch
sizes, or allowed these patches to overlap, inferences ultimately
were degraded and processes were not derivable from pattern
The message here is important: pattern does not map 1: 1 with
generating processes, and so for complex (i.e., real) systems the
logical coupling whereby we emphasize pattern as the key to
underlying processes should not be over-interpreted. This is a
crucial point, as an implicit working hypothesis in landscape (or
ecosystem) management seems to be, Save the pattern and you'll
save the process as well. Nonetheless, it is pattern that we know
best, and for which we have the most readily available data (e.g.,
maps and surveys). And so, it is still reasonable to attempt to
base a management strategy on maintaining landscape pattern
(2) Pattern-based approaches can be extended by
explicitly considering the agents of pattern
formation on landscapes.
(3) Landscape (ecosystem) managers must invest
heavily in models, especially spatial simulators, as
tools for exploring alternative scenarios for systems
that cannot be manipulated easily.
Pattern and Process in Ecology
,
.... .,'
Much of ecology today laOors under the "pattern-process
paradigm," which might be loosely stated as: Ecological
processes generate patterns, and by stUdying these patterns we
can make useful inferences about the underlying processes.
There is an implicit concession here that it is actually the
processes we are most concerned about, but these are often too
difficult (perhaps for logistical ;reasons) to study directly. Thus,
we measure the result of these. processes, and infer the rest
Landscape ecology labors under an additional onus, in that
we recognize that pattern constrains ecological processes,
providing a feedback between generating process, resultant
pattern, and constrained process (Turner 1989). To my
knowledge, landscape ecologists have not explicitly considered
the extent to which ecological processes can be inferred from
measured pattern in this feedback relationship. To be fair, the
discipline has probably invested more in descnbing pattern and
its implications than in explaining how pattern actually develops.
I digress about pattern and process for this simple reason: I
believe we may limit ourselves by emphasizing pattern itself,
and we should be investing more effort to understand how
ecological processes work. Much (most?) of our theory is about
pattern; much less so, about processes. For example, we have a
"law" about the relationship between stand biomass and density
(the -312 thinning law), but the precise reasons for this law--the
processes generating it-are somewhat debatable (Weller 1987).
Likewise, species-area relationships are readily observable
patterns in nature, but the underlying processes-and there are
several-are not always obvious (Conner and McCoy 1979).
The list of examples could go on: we observe log-normal
distributions of species abundances (why?), and so on
A few studies have looked into the inference of process from
pattern, and results suggest we should not push such inferences
too far. Cale et al. (1989) studied a simple model of two
populations to determine whether the generating processes
(competition and reproduction) could be inferred from observed
pattern (species abundance). Even in their model, they found
that it was difficult to infer the relative importance of the
underlying processes: patterns were not isomorphic (different
processes could generate similar patterns), the modeled
processes sometimes yielded patterns that appeared random, and
in a few cases the pattern suggested an inference which was
simply incorrect. In another study, Moloney et al. (1992) used
a simple distuIbance model to assess whether distuIbance
parameters (patch size) could be inferred from the resultant
PATTERN PRESERVATION AS A
MANAGEMENT GOAL
The ideal goal in managing a landscape based on its pattern
may be to maintain a statistically stationary pattern over time.
This, of course, requires that the reference pattern be defmed
beforehand, in tenns relevant to the management objectives
(timber classes, habitat types, or whatever). The notion of a
"stable" landscape (or ecosystem) as a statistically stationaty
pattern (however defmed) is as fundamental to ecology as the
pattern-process paradigm itself (Watt 1947). This concept has
been rediscovered repeatedly by ecologists recently (Bonnann
and Likens 1979, Shugart and West 1981, UIban et al. 1987,
Turner et al. 1993).
The "Unit Pattern" as a Model System
In his seminal paper, Watt (1947) emphasized the relationship
between demographic processes (establishment, growth, and
mortality) and forest pattern (distribution of forest age classes
or seral stages). Watt defined the "unit pattern" as the basic
entity of the forest community-a full representation of the
pattern in all its phases (fig. 1). The unit pattern is a two-levelled
depiction of a forest: at a fine scale, each patch-scale element
of the community is undergoing continual change, yet at a larger
scale the distribution of patch types--the pattern-is stationary.
This depiction was later developed as the "shifting-mosaic
steady state" for northern hardwood forests (Bonnann and
Likens 1979); it was further extended by Shugart (1984), and
has been illustrated in a statistical framewoIk by Smith and
UIban (1988).
While Watt's focus was the plant community, this same logic
extends to landscapes or ecosystems. Indeed, Whittaker's (1953)
redefinition of the "climax" as a stationary distribution of
various successional stages and edaphic types is as appropriate
a model for landscape pattern as any defmition more recent
landscape ecologists have proposed (UIban et al. 1987). To apply
128
so
10
Bare
Oxo1"
Rubw
Bare
Rubus
(cz)
Gap
Oxalis
(b)
Figure 1. - Watt's concept of the unit pattern: (a) idealized successional pattern and (b) this pattern as a distribution in space "when
the old wood is left to itself" (Watt 1947:14).
So if this strategy is so simple, why don't we do it already?
The fact is, the strategy is simple but actually fulfilling this
the unit pattern concept more fully to landscapes, it merely must
be extended to include the primaty agents of patch fonnation
on landscapes. These agents are biotic processes (e.g.,
demographics and competition), abiotic constraints (edaphic
pattern, topographic constraints), and disturbances (see below).
Two implications of the unit pattern are pertinent here. First,
a sample of a system (landscape or ecosystem) smaller than the
unit pattern is an inadequate representation of the system in the
sense that it cannot represent all of its phases. Secondly, in a
constant environment and over a sufficiently large area, a system
will show a steady state of definite proportions among
constituent phases, with the area in each phase in proportion
with the duration of the phase. This latter notion (Watt's "phasic
equilibrium") is the exact goal of sustainable management.
Thus, an obvious goal in managing an ecosystem (or
landscape) is simply to preserve the unit pattem This strategy
is neither profound nor novel. Indeed, one of the basic tenets of
timber management in forestIy is to maintain a statiOnaIy age
distnbution across cutting units, as this ensures sustained yield.
This is the so-called" fully-regulated forest" in modem forestIy
(Davis 1966), or the "nonnal forest" for Gennan foresters of
centuries ago.
strategy is much less simple. The area required to stabilize a
distribution of habitat types can be estimated by simulation, in
a way exactly analogous to constructing a cumulative variance
curve to estimate a minimum sample size in study design.
Shugart and West (1981) 'and Urban et al. (1987) provided
heuristic examples whereby they estimated the land area needed
to ensure stationarity for systems driven by episodic disturbances
(fig. 2). In many cases, the temporal variability was such that
the implied unit pattern was much larger than the area available
as bounded reserves (e.g., National Forests or Patks). That is,
the ideal goal can probably be realized for vety few systems.
Indeed, the example of the nonnal forest is perhaps one of vety
few cases where the goal of stationarity can be met in a real
system, and only then if there are no latger-scale disturbances
acting on the system.
Thmer et al. (1993) used a simulation model to further explore
the idea of landscape-scale variability for systems driven by
disturbances. In their model, the landscape vegetation (which
grows deterministically on a featureless landscape) has a
recovety time t indexing the rate of succession, and the
129
8
7
.....
-
N
E 6
~
0
.J
......
c(
..-
WILDFIRE
5
UJ
a:
c(
WINDTHROW
4
..
UJ
0
Z
c(
....
TREEFALL
m 3
a:
:l
....
~
c
2
t t
1
STAND WATERSHED
t
PARKS
0
0
1
2
3
4
7
5
6
2
FOCAL -AREA [LOG(m )]
8
9
10
Figure 2. - Landscape equilibrium as a function of disturbance scale and containing area (from Urban et at 1987). The diagonal, a 60:1
ratio which was found to be sufficient to statistically stabilize results from a forest succession model, is used to illustrate
approximate transition from equilibrium to inherently nonequilibrium systems.
disturbance has a recurrence interval r and a spatial extent or
size s. TIley normalized scaling parameters into two ratios: a
temporal scaling parameter T (= r/t), and a spatial parameter S
(= sM, where A is total landscape area). This way, any
disturbance regime can be normalized temporally (its recurrence
interval relative to system recovery time) and spatially (its size
relative to the containing area). In simulations of various
disturbance regimes, they found that landscape dynamics fell
into a few qualitative domains in the scaling parameter space
(fig. 3):
(1) Systems with relatively small disturbances exhibited
more-or-less equilibrium conditions regardless of
disturbance frequency; the disturbance events were
simply absorbed by the landscape.
(2) Systems driven by large, infrequent disturbances
showed nonequilibrium dynamics wherein the
landscape reflected each disturbance event as a
perturbation.
(3) Systems driven by large, frequent disturbances
exhibited a quasi-equilibrium in which the
landscape was quite dynamic but remained within
stable bounds.
Note that only in the restricted case of small, frequent
disturbances are the conditions of the unit pattern met; in no
other case is a stationary distribution of patch types expected.
And note that this example is itself a simple case: a simple
model with no topographic or edaphic complexities, and unifonn
disturbances.
Turner et al.' s model experiments offer some guidelines of
what we might expect from a system, given the scaling
parameters of its disturbance regime and successional dynamics.
Clearly, for many systems the expectation is not a stationary
landscape pattern The recent Yellowstone fires underscore this
conclusion for a system which has never shown a stationary
configuration over the past several centuries (Romme 1982).
Certainly, designs for a regulated forest become rather
130
c
Q) GI
ES-
Q)li
ES-
equilibrium
~J:
~ ~
~:E
~
o(J
quasiequilibrium
~.5
~
(J
Q)
-~
IEJ····································
1j
-~
Q)
Urne
a:
a:
......
...
.E
.....
Q)
............
IDZJ
Q)
(J
Q)
(J
c
c
CIS
CIS
.........
.c
=-
JLJ
time
...
.E
Q)
~ ~
ro~
.c
~
............
...............
:l_
]!
nonequilibrium
. _. . _. ._. ________________________________
J:~:_~
oS-
~
J:~
time
__________________________________
~
large
small
large
small
~
Disturbance Size/Landscape Extent
Disturbance Size/Landscape Extent
Figure 3. - Landscape dynamics under varying disturbance regimes normalized by temporal and spatial scale (modified from Turner
et at 1993). (a) Domains in which landscape dy~amics can be viewed as equilibrium, quasi-equilibrium, and nonequilibrium. The
dotted lines in the figure arbitrarily separate domains that grade into one another. (b) Typical dynamics one might expect from a
system in each of these domains, i.e., their natural ranges of variability.
Biotic Processes
superfluous for landscapes driven by large, episodic distutbances
such as hurricanes. Turner et al. (1993) discuss several natural
and human-modified landscapes relative to their scaling
parameters.
Biotic processes include plant demography (dispersal,
establishment, growth, and mortality) and competition It is
important to note that even in a perfectly homogeneous
environment, demographic processes over time would gererate
spatial reterogeneity. Indeed, the mechanism of pattern formation
via plant growth and mortality was tOO basis of Watt's (1925, 1947)
seminal ideas on pattern and process in plant communities.
Plant dispersal can act as an agent of pattern fonnation,
particularly when coupled to differential rates of other population
processes (especially mortality). This mechanism is addressed
in a huge literature on diffusive instabilities in diffusion-reaction
systems (e.g., Okubo 1980, Kareiva 1990).
Competition figures prominently in the generation of
vegetation pattern Because this differential success depends
strictly on the environmental context of competition, it is
necessaty to consider these effects with reference to local
patterns of abiotic constraints (see below).
EXTENSIONS TO PATTERN
MANAGEMENT
The pattern-based approach provides useful insights about
the feasibility of maintaining a particular landscape in a
steady-state condition. This conceptual framework can also
be used to suggest guidelines for "rescaling" systems to
effect their qualitative dynamics (Urban et al. 1987).
Rescaling a fire regime via smaller, less intense, prescribed
burns that might be sustained within a bounded region is a
familiar example in current practice. The pattern-based
approach can be extended further by considering explicitly
the mechanisms that generate landscape pattern, and using
these extensions as a further guide to managing complex
landscapes.
Abiotic Constraint
Real landscapes are patterned by spatially heterogeneous
features including soil catenas, topography, and other
environmental gradients. Many of these aspects of
landscape pattern are addressed in classical gradient
analysis (Whittaker 1967, Gauch 1982). A long tradition
of gradient analysis has identified two predominant axes
of vegetation pattern on landscapes: temperature (often
indexed as elevation) and relative moisture (often indexed
as slope aspect or exposure, or soil depth). These features
provide a template on which other pattern-forming
processes act. Gosz (1992) has advocated using gradient
analysis as a framework for exploring scenarios of
landscape change.
Agents of Pattern Formation
Landscape pattern is generated by the interplay of three
general agents: biotic processes, abiotic constraints, and
disturbance. The first two are coupled inseparably in
vegetation pattern, while disturbance can sometimes be
decoupled and overlaid onto the system, depending on one's
frame of reference (Allen and Starr 1982, Urban et al. 1987).
131
most xeric site, only the most drought-tolerant species can
smvive, and it characterizes the sere at all ages. On less xeric
sites, there is a classical pattern of species replacement, from
the fastest-growing/most intolerant, to successively
slower-growing but more tolerant species. On a mesic site, the
species with the fastest growth rate dominates in early
succession, while the most shade-tolerant species ultimately
dominates old-growth (" climax") stands. In general, the
succession is from the fastest-growing species for a given
soil-moisture regime, to the next-fastest/next more tolerant
for that site, and so on, to the most shade-tolerant species
that can persist I under that particular soil moisture regime.
Because of life-history trade-offs (premise 3), seral patterns
dictated by available light are related to spatial patterns in
soil moisture. Thus, explanations of vegetation dynamics
in time (succession) must be interpreted with respect to
their position in space, along environmental gradients.
Austin and Smith (1989) link these arguments more
explicitly to classical gradient analysis.
Disturbance
Our thinking about distuIbance has evolved considerably over
the past few years, from earlier notions of disturbances as
events from "outside the system" that disrupted things and
were therefore "bad," to an acceptance of these events as a
natural and integral component of the system (pickett and
White 1985).
A consideration of the spatial and temporal scaling of
disturbance regimes has led to a further elaboration of
disturbance as a component of ecosystems, in which a system
can be referenced at two levels of organization (and hence,
two scales). At a lower level, disturbances are "outside" and
disruptive while at a higher level, they are incorporated into
the system-they are "inside" and not distuIbing at all (Allen
and Starr 1982, O'Neill et al. 1986, Urban et al. 1987). This
two-level depiction of disturbance lends itself nicely to an
extension of the unit pattern concept from stands to
landscapes. By this strict ~efinition, a landscape has a
stationary pattern at that spatial scale that can II average
away II the perturbations associated with individual
disturbance events.
~
r---------------------------------------------~
C)
:c
".
Interactions and Feedbacks
One of the reasons landscapes are complex is that each of
these agents of pattern formation interacts with the others. In
particular, vegetation pattern cannot be interpreted without
reference to demographic processes in the context of
environmental gradients. Smith and Huston (1989, see also
Huston and Smith 1987) used a simulation model to illustrate
this interaction. Their model was an individual-based forest
simulator (Shugart and West 1980, Huston et al. 1988) which
was simplified to emphasize tree competition for light on sites
along a soil moisture gradient. Smith and Huston proceeded
from three premises about tree life-history traits, which reflect
anatomical, morphological, and physiological trade-offs in
plant strategies: (1) a species tolerant of low resource levels
(e.g., shade, or low soil moisture) would have a lower
maximum growth rate than intolerant forms (i.e., tolerance
implies low maximum growth rate); (2) conversely, a species
with a high maximum growth rate under favorable resource
levels would have less tolerance to reduced resource levels
(i.e., high maximum growth rate implies low tolerance) (fig.
4a), and (3) a species cannot simultaneously optimize for
tolerance to reduced above- and below-ground resources, i.e.,
a shade-tolerant tree cannot also be drought-tolerant (although
a shade-intolerant tree can be drought-intolerant as well).
These three premises imply a species response space (fig. 4b)
which Smith and Huston represented with 15 hypothetical
species (fig. 5a), In simulations, interactions among these
species were sufficient to generate classical successional
patterns in species replacement as well as gradient response
in space (fig. 5b). These patterns obtain as follows: On the
tolerant
3:
£
~------~----~----~--..-...-...~.--------~----~
high
-g,
................
..............
.......
e
C
low
Resource Availability
......
tOleranc:··;;·~it
.g, combined
.........
:c
______ ____ ____ ______
____ ____
~
high
~
~
~
~
Shade Tolerance
~
~
low
Figure 4. - Response space for tree species life-histories, relative
to available light (shade tolerance) and soil moisture
(drought tolerance). (a) For either resource, tolerance comes
at the expense of reduced maximum growth rate. (b) These
trade-offs arrayed along two axes: maximum growth rates
increase toward the upper right, while tolerance increases
to the opposite corners; no species can be very tolerant of
drought and shade simultaneously and so the lower, left
corner is devoid of species (after Smith and Huston 1889).
132
1
LOWe
ow
~
g
OW
Wa:
a:::J
o~
~!l2
wO
o~
z
«
a:
W
....J
2
3
4
e--e--e--e
6
e
~
7
e
10
e
~
°
~
HIGH
HIGH
SHADE TOLERANCE
8
e
11
e
13
e
5
20
\"(8)
o ~_________________________~15~
19
DAY
_____1;':";~:
3: b---~-:
e
112
e
40
(c)
10
114
e
~115
o ~=!5ii::::=---===_....b::======0115
LOWe
60
(d)
8
Figure 6(a). -Implications of Iife-hiStory trade-offs for succession
and gradient response (from Smith and Huston 1989).
Hypothetical species invented to span the "triangle" implied
by life-history trade-offs (see text and fig. 4b).
Distmbance, of course, interacts with the other agents of
pattern formation. Fire is a familiar example of a spatially and
temporally correlated disturbance regime: fires burn
differentially with respect to topography, fuel type, fuel load
(forest age or condition), and so on. Thus, distutbances interact
with biotic processes and abiotic constraints, as well as with
other distutbances (e.g., Knight 1987).
Dispersal can generate pattern by itself, but it also may have
a secondaIy effect as a local intensifier of patterns generated by
other agents. Thus, dispersal may act as a positive feedback
mechanism in pattern formation, reinforcing and amplifying
initial pattem
o
100
~~~~
__
-L~~~~-....l
(f)
Implications of Agents of Pattern Formation
This discussion of pattern-generating agents in general, and
the interplay of biotic processes with abiotic gradients in
particular, has implications for managing landscapes for
biodiversity. The simulations of Smith and Huston (1989)
predicted that a sere includes more species on a mesic site than
on a xeric site. This, in tum, suggests that for a landscape
characterized by rather unifonnly mesic sites, diversity would
be maximized by managing for stands of vatying ages because
old stands tend to be dominated by the same species. Conversely,
for a landscape of more heterogeneous (and mostly xeric) site
conditions, older stands would likely be dominated by a greater
variety of species because the seres would have various
endpoints; diversity would be increased by maintaining a set of
stands on different kinds of sites. Thus, the management
prescription in the fonner case is for activity in the time domain;
in the latter case, in the spatial domain This prescription is
50
8
2
100
200
300
WET
400
YEAR
Figure 6(b). - Successional trends for these species, as
simulated for sites along a soil moisture gradient. In general,
succession proceeds from the most shade-intolerant/fastest
growing, to slower..growing, more tolerant species that can
persist under the soil moisture regime on each site, and so
proceeds from right to left along the rows in (a).
133
borne of a consideration of how biotic arid abiotic agents interact
to generate patterns in species diversity (see also Gosz 1992 for
similar conclusions).
This prescription is an example of how we might add an
explicit consideration of pattern-generating mechanisms to
enrich a management strategy based on pattern by itself. This·
extension is still amenable to the approach of predicting the
qualitative dynamics of a reference area (management unit,
forest, patk) given the scaling parameters of its successional
dynamics, abiotic template, and distwbance regime (following
Turner et al. 1993). Likewise, this approach could be extended
further to consider even more detailed (realistic) biotic processes,
multiple environmental gradients, and various distUIbances
(including management), and so embrace more fully the agents
of pattern formation on landscapes. In general, the approach
remains the same but the simulations become more complicated.
Because of this complexity, these extensions demand a new set
of tools for researchers and nuptagers alike.
In general, ecosystem management is not an optimization
problem. Part of the reason for this is implicit in the concept of
"natural range of variability." The goal to maintain a system in
some semblance of normalcy seems inconsistent with a
simultaneous goal to maximize any particular aspect of the
system But a second reason problem with optimization is that
we simply lack the tools: we do not have, and in the future we
are not likely to have, modeling tools that reconcile disparate
ecosystem attributes in a common currency. A model that
provides useful predictions about wildlife habitat is not likely
to have much to say about water quality~ a stand yield model
will likely be m~t on butterfly diversity. While our policy goal
may transcend "multiple use" to embrace the full complexity
of ecosystems, our best models focus on single (or a very few)
uses and will likely remain so.
The ultimate tool for ecosystem management might be some
sort of marriage between geographic information systems (GIS),
ecological simulators, and decision support models (e.g.,
Covingtonet al. 1988, van Voris et al. 1993). Ecological models
would provide a means to assess alternative management
prescriptions or other dynamic scenarios (e.g., climate change).
A GIS would serve as a framework for data storage,
manipulation, and display (e.g., storing stand smvey data and
highlighting stands meeting user-specified· criteria). A user
interface incorporating decision support tools would allow a
researcher or manager to move interactively among all
components of the system. This goal implies new technological
developments, and new training for resource managers and basic
scientists as well. But ecosystem management seems to call for
new tools and approaches, and we would do ourselves a
disseIVice to ignore this challenge.
MODELS IN ECOSYSTEM MANAGEMENT
Accounting for landscape pattern in space and time is an
obvious challenge, and it seems equally obvious that models
will play a crucial role in this approach. Behan (1990) has
emphasized that the conceptual model, hence computerized
tools, of multi~le-use management are qualitatively different
from that of su's.tained-yield approaches of single-commodity
management. I wQuld argue that the challenges of landscape
ecology and ecosystem management will require still another
generation of modelfug tools.
Consider the sorts of models I've used here as illustrations:
these range from fairly detailed, nonspatial simulators (Smith
and Huston 1989) to simpler but spatial simulators (Turner
et al. 1993). The current trend seems to be toward simulators
that are spatially explicit and incorporate a wealth of
ecological detail (Baker 1989, 1992~ Sklar and Costanza
1991). These are new kinds of models, and we're only now
learning how to use them cleverly~ there are computational
as well as ecological issues to resolve. Appropriately, there
is a great diversity of approaches being pursued, which will
ensure that a variety of useful and robust models will become
more available to end-users.
These simulation models are used in a different way than
optimization models used in planning (e.g., FORPLAN). In
planning models, an objective function is specified and the best
solution is computed based on the specified constraints. By
contrast, landscape simulators are used in an exploratory mode:
Is this scenario betterlworse than this alternative scenario?
Does this management prescription maintain more old-growth
than the alternative? Will this cutting pattern generate more
edge over the long run? If we do this, what will happen to
wildlifo habitat? Will water quality suifor? What might happen
if we try this instead? These are not really questions about
optimization
CONCLUSIONS
As with the nascent field of landscape ecology, ecosystem
management is new and exciting. It is also uncertain, simply
because we don't really know what we're doing~ we have no
historical precedent, and no real frame of reference by which to
judge our success. Because of this uncertainty, we have to plan
on learning as we go, using our management decisions as
experiments from which to learn With careful planning and the
aid of models, these experiments can be as controlled and
well-replicated as resources allow. Presumably, models will
minimize the incidence of "unpleasant surprises" in
management experiments, but we must also retain the flexibility
to learn from our mistakes and take corrective action: this is the
essence of adaptive management. But this is also the scientific
method, and a partnership whereby managers helped perfonn
experiments with researchers certainly would be to everyone's
advantage.
A basic appreciation of landscape dynamics, and hence of
ecosystem management as practiced on landscapes, leads to the
conclusion that old-fashioned notions of "the constancy of
nature" are not likely to apply to real landscapes (Botkin 1990).
134
Huston, M., D.L. DeAngelis, and W.M. Post. 1988. New
computer models unify ecological theol)'. BioScience
38:682-691.
Huston, M.A. and T.M. Smith. 1987. Plant succession: life
histol)' and competition Am. Nat. 130:168-198.
Kareiva, P. 1990. Population dynamics in spatially complex
environments: theol)' and data. Phil. Trans. Royal Soc.
London B 330:175-190.
Kessler, W.B., H. Salwasser, C.W. Cartwright, and J.A. Caplan
1992. New perspectives for sustainable natural resources
management. Ecol. Applic. 2:221-225.
Knight, D.H. 1987. I Parasites, lightning, and the vegetative
mosaic in wilderness landscapes. Pages 59-83 in M.G. Turner
(ed.), Landscape heterogeneity and disturbance.
Springer-Verlag, New YOlk
Moloney, K.A., A. Morin, and S.A. Levin 1992. lnteIpreting
ecological patterns generated through simple stochastic
processes. Landscape Ecol. 5:163-174.
Okubo, A. 1980. Diffusion and ecological problems:
mathematical models. Springer-Verlag, New York.
O'Neill, R.V., D.L. DeAngelis, J.B. Waide, and T.F.H. Allen
1986. A hierarchical concept of the ecosystem. Princeton
Univ. Press, Princeton, NJ.
Pickett, S.T.A., and P.S. White (eds.). 1985. The ecology of
natural disturbance and patch dynamics. Academic Press,
Orlando, FL.
Risser, P.G., J.R. Karr, and R.T.T. Forman 1984. Landscape
ecology: directions and approaches. Special Publ. No.2, TIL
Natural Hist. Surv., Champaign
Romme, W.H. 1982. Fire and landscape diversity in subalpine
forests of Yellowstone National Park. Ecol. Monogr.
52:199-221.
Shugart, H.H., and D.C. West. 1980. Forest succession models.
BioScience 30:308-313.
Shugart, H.H., and D.C. West. 1981. Long-term dynamics of
forest ecosystems. Am. Scientist 69:647-652.
Sklar, F.H., and R. Costanza. 1991. The development of
dynamic spatial models for landscape ecology: a review and
prognosis. Pages 239-288 in M.G. Turner and R.H. Gardner
(eds.), Quantitative methods in landscape ecology.
Springer-Verlag, New Yolk.
Smith, T.M. and M. Huston. 1989. A theOl)' of the spatial and
temporal dynamics of plant communities. Vegetatio 83:49-69.
Smith, T. M., and D. L. Urban 1988. Scale and resolution of
forest structural pattern Vegetatio 74:143-150.
Turner, M.G. 1989. Landscape ecology: the effect ofpattem on
process. Ann. Rev. Ecol. Syst. 20:171-197.
Turner, M.G., and R.H. Gardner (eds.). 1991. Quantitative
methods in landscape ecology. Springer-Verlag, New Yolk.
Turner, M.G., W.H. Romme, R.H. Gardner, R.V. O'Neill, and
T.K. Kratz. 1993. A revised concept of landscape equilibrium:
distwbance and stability on scaled landscapes. Landscape
Ecol. (in press).
UIban, D.L., R.V. O'Neill, and H.H. Shugart. 1987. Landscape
ecology. BioScience 37:119-127.
This presents an educational challenge to ecosystem managers:
we need to convince the public (and perhaps ourselves) that it
is acceptable for nature to behave erratically, for landscapes not
to look the same year after year. We have been reasonably
successful in retraining the public about the role offIre in natural
ecosystems, and so there is reason to be optimistic about the
role of education in ecosystem management. But novelty is not
always welcome, and so we must be aggressive in pursuing the
change to the new, ecosystems perspective.
ACKNOWLEDGEMENTS
The author is supported by National Science Foundation
Grant No. BSR-9013888 and Cooperative Agreement
CA8000-1-9004 with the National Park Service. I appreciate
Dan Binkley's helpful if skeptical discussion and comments on
this manuscript.
LITERATURE CITED
Allen, T.F.H., and T.B. Starr. 1982. Hierarchy: perspectives for
ecological complexity. Univ. Chicago Press, Chicago.
Austin, M.P., and T.M. Smith 1989. A new model for the
continuum concept. Vegetatio 83:35-47.
Baker, W.L. 1989. A review of models of landscape change.
Landscape Ecol. 2:111-133.
Baker, W.L. 1992. The landscape ecology of large distwbances
in the design and management of nature reselVes. Landscape
Ecol. 7:181-194.
Behan, R.W. 1990. Multiresource forest management: a
parndigmatic challenge to professional forestIy. J. ForestIy
88:12-18.
Bonnann, F.H., and G.E. Likens. 1979. Pattern and process in
aforested ecosystem. Springer-Verlag, New York.
Botkin, D.B. 1990. Discordant harmonies: a new ecology for
the twenty-first century. Oxford Univ. Press, New YOlk
Cale, W.G., G.M. Henebty, and J.A. Yeakley. 1989. Inferring
process from pattern in natuIal communities. BioScience
39:600-605.
Conner, E.F., and ED. McCoy. 1979. The statistics and biology
of the species-area relationship. Am. Nat. 113:791-833.
Covington, W.W., D.B. Wood, D.L. Young, D.P. Dykstra, and
L.D. Garrett. 1988. TEAMS: a decision support system for
multiresource management. J. For. 86:25-33.
Davis, K.P. 1966. Forest management: regulation and valuation
McGraw-Hill, New York.
Gauch, H.G. 1982. Multivariate analysis in community ecology.
Cambridge Univ. Press, Cambridge.
Gosz, J.R. 1992. Gradient analysis of ecological change in time
and space: implications for forest management. Ecol. Applic.
2:248-261.
135
Weller, D.E. 1987. A reevaluation of the }/2 power rule of plant
self-thinning. Beo1. Monogr. 57:23-41.
Whittaker, R.H. 1953. A consideration of climax theory: the
climax as a population and pattern Beol. Monogr. 23 :41-78.
Whittaker, R.H. 1%7. Gradient analysis of vegetation BioI.
Rev. 49:207-264.
Wiens,1.A. 1992. What is landscape ecology, really? Landscape
Eco1. 7:149-150.
van Voris, P., W.D. Millard, 1. Thomas, and D.L. Utban. 1993.
TERRA-Vision--the integration of scientific analysis into the
decision-making process. Int. J. Geographical Infonnation
Systems 7:143-164.
Watt, A.S. 1925. On the ecology of British beechwoods with
special reference to their regeneration 1. Beo1. 13:27-73.
Watt, A.S. 1947. Pattern and process in the plant community.
1. Eco1. 35:1-22.
136