34
gional levels. Change may be the normal turnover
in species in a forest that changes in time or in space
with the formation of gaps and the resulting successional dynamics. Alternatively, change may be
anthropogenic ally induced, such as the clearing of
an agricultural field or the harvesting of timber
from a forest. Detection and analysis of change to
the structure and function of the system are critical. Prediction turns on our ability first to identify
change or trends in data and then on our ability to
understand the meaning of these changes for
ecosystem function at all levels in the hierarchy.
2.4 System Dynamics
Landscape change over time requires us to examine the theoretical basis for uriderstanding ecosystem dynamics. The conceptual underpinnings of
ecologists' understanding of ecological dynamics
have shifted dramatically in recent decades. Earlier
theories of ecosystems focused on equilibrium
models, often expressed verbally in terms of the
"balance of nature" ( Egerton, 1973; Pimm, 1991;
Wu and Loucks, 1995). Recent decades have seen
the emergence of a number of alternative paradigms for describing ecological dynamics (Botkin,
1990; Pahl-Wostl, 1995; Wu and Loucks, 1995).
We will not explore these theories in depth, but
rather give a brief overview suggesting the breadth
of the perspectives that may be applicable to the
analysis of ecosystem or landscape dynamics. The
reader should bear in mind that the applicability of
these various theories depends on the spatiotemporal scale at which the system is considered. Different theories represent different ways of viewing
the system. Thus the choice of which theoretical
approach to apply to a given situation will depend
on the phenomena of interest, as well as on the spatial and temporal scales.
2.4.1 Simple Dynamics
Consider a forest at a regional scale. At any point
in time, some areas of the forest may be experiencing low endemic levels of pests, while others
may experience epidemic outbreaks that kill trees.
Part of the forest may be on fire, while another part
may not have experienced fire for decades or centuries. Each of these are functional states of the forest that are happening right now at different points
in space. These states may also be realized in one
location at different times. There is potential for
multiple equilibria (or states) of an ecosystem with
the possibility of sudden (surprising, perhaps discontinuous) transitions from one state to another
A Theoretical Framework for Ecological Assessment
(e.g., Holling, 1986). Each of these equilibria acts
as an attractor toward which the system moves over
time. From a management perspective, the significance of these transitions varies. In some cases, certain equilibria (i.e., forest without fire or pests) may
be deemed desirable, while others are considered
disastrous. The management objective is to keep
the system out of the undesired states and their domains of attraction. On the other hand, certain shifts
between domains of attraction may be important in
the long-term dynamics of natural systems.
Holling uses the term resilience to refer to the
capacity of an ecosystem to shift to mUltiple states
or domains in a manner that ensures the long-term
adaptability and continued functioning of the system in the face of perturbations (Holling, 1986). In
this context, proper management would allow transitions. Attempts to maintain a system in one desired state are likely to be unsuccessful in the long
term. A system restricted to a particular attractor
will eventually become fragile and will ultimately
collapse when exposed to a perturbation for which
the system lacks an adaptive response (Holling and
Meffe, 1996). That is, when it is natural for an
ecosystem to move back and forth between states
such as fire-no fire, then it is a mistake to try to
confine it to a single state, because the system will
lose its ability to incorporate small disturbances
into the normal function of the forest. Thus we have
begun to rethink our management practice of fire
suppression. If we had had these theories to consider at the point that managers initially decided to
suppress fire in the Yellowstone ecosystem, we
may have been able to anticipate the eventual catastrophic bum. The key point for assessment is to
identify the scale at which the system is to be managed, along with the other scales of the system that
management decisions may affect.
At times ecosystem dynamics converge not toward a fixed state (i.e., a mathematical equilibrium), but toward a fixed cycle of states. Predatorprey models often predict periodic fluctuations in
species densities, and empirical data suggest that
such behavior is a feature of some real-world systems (Pimm, 1991). Mathematically, the dynamics
are governed by an attractor that exhibits cyclic behavior. Assessment strategies for systems with expected cyclic behavior will have to carefully consider
the time scale of investigation relative to the time
scale of the system's natural cycles (periodicities).
2.4.2 Chaotic Dynamics
Systems that display chaotic dynamics converge toward a strange attractor. Attractors are strange because they are a complex fractal, rather than the
gional levels. Change may be the normal turnover
in species in a forest that changes in time or in space
with the formation of gaps and the resulting successional dynamics. Alternatively, change may be
anthropogenic ally induced, such as the clearing of
an agricultural field or the harvesting of timber
from a forest. Detection and analysis of change to
the structure and function of the system are critical. Prediction turns on our ability first to identify
change or trends in data and then on our ability to
understand the meaning of these changes for
ecosystem function at all levels in the hierarchy.
2.4 System Dynamics
Landscape change over time requires us to examine the theoretical basis for uriderstanding ecosystem dynamics. The conceptual underpinnings of
ecologists' understanding of ecological dynamics
have shifted dramatically in recent decades. Earlier
theories of ecosystems focused on equilibrium
models, often expressed verbally in terms of the
"balance of nature" ( Egerton, 1973; Pimm, 1991;
Wu and Loucks, 1995). Recent decades have seen
the emergence of a number of alternative paradigms for describing ecological dynamics (Botkin,
1990; Pahl-Wostl, 1995; Wu and Loucks, 1995).
We will not explore these theories in depth, but
rather give a brief overview suggesting the breadth
of the perspectives that may be applicable to the
analysis of ecosystem or landscape dynamics. The
reader should bear in mind that the applicability of
these various theories depends on the spatiotemporal scale at which the system is considered. Different theories represent different ways of viewing
the system. Thus the choice of which theoretical
approach to apply to a given situation will depend
on the phenomena of interest, as well as on the spatial and temporal scales.
2.4.1 Simple Dynamics
Consider a forest at a regional scale. At any point
in time, some areas of the forest may be experiencing low endemic levels of pests, while others
may experience epidemic outbreaks that kill trees.
Part of the forest may be on fire, while another part
may not have experienced fire for decades or centuries. Each of these are functional states of the forest that are happening right now at different points
in space. These states may also be realized in one
location at different times. There is potential for
multiple equilibria (or states) of an ecosystem with
the possibility of sudden (surprising, perhaps discontinuous) transitions from one state to another
A Theoretical Framework for Ecological Assessment
(e.g., Holling, 1986). Each of these equilibria acts
as an attractor toward which the system moves over
time. From a management perspective, the significance of these transitions varies. In some cases, certain equilibria (i.e., forest without fire or pests) may
be deemed desirable, while others are considered
disastrous. The management objective is to keep
the system out of the undesired states and their domains of attraction. On the other hand, certain shifts
between domains of attraction may be important in
the long-term dynamics of natural systems.
Holling uses the term resilience to refer to the
capacity of an ecosystem to shift to mUltiple states
or domains in a manner that ensures the long-term
adaptability and continued functioning of the system in the face of perturbations (Holling, 1986). In
this context, proper management would allow transitions. Attempts to maintain a system in one desired state are likely to be unsuccessful in the long
term. A system restricted to a particular attractor
will eventually become fragile and will ultimately
collapse when exposed to a perturbation for which
the system lacks an adaptive response (Holling and
Meffe, 1996). That is, when it is natural for an
ecosystem to move back and forth between states
such as fire-no fire, then it is a mistake to try to
confine it to a single state, because the system will
lose its ability to incorporate small disturbances
into the normal function of the forest. Thus we have
begun to rethink our management practice of fire
suppression. If we had had these theories to consider at the point that managers initially decided to
suppress fire in the Yellowstone ecosystem, we
may have been able to anticipate the eventual catastrophic bum. The key point for assessment is to
identify the scale at which the system is to be managed, along with the other scales of the system that
management decisions may affect.
At times ecosystem dynamics converge not toward a fixed state (i.e., a mathematical equilibrium), but toward a fixed cycle of states. Predatorprey models often predict periodic fluctuations in
species densities, and empirical data suggest that
such behavior is a feature of some real-world systems (Pimm, 1991). Mathematically, the dynamics
are governed by an attractor that exhibits cyclic behavior. Assessment strategies for systems with expected cyclic behavior will have to carefully consider
the time scale of investigation relative to the time
scale of the system's natural cycles (periodicities).
2.4.2 Chaotic Dynamics
Systems that display chaotic dynamics converge toward a strange attractor. Attractors are strange because they are a complex fractal, rather than the