New Models for Ecosystem Dynamics and Restoration

New Models for Ecosystem Dynamics and Restoration

by Richard J. Hobbs

Conceptual models based on alternative stable states and restoration thresholds can help inform restoration efforts. New Models for Ecosystem Dynamics and Restoration brings together leading experts from around the world to explore how conceptual models of ecosystem dynamics can be applied to the recovery of degraded systems and how recent advances in our

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Conceptual models based on alternative stable states and restoration thresholds can help inform restoration efforts. New Models for Ecosystem Dynamics and Restoration brings together leading experts from around the world to explore how conceptual models of ecosystem dynamics can be applied to the recovery of degraded systems and how recent advances in our understanding of ecosystem and landscape dynamics can be translated into conceptual and practical frameworks for restoration.

Editorial Reviews


"This is a very thorough, extensively researched, and well-written book, whose scope is not hindered by the abundance of case studies from Australian systems."

-- L. S. Rigg, Illinois University

— L. S. Rigg

Choice - L. S. Rigg
"This is a very thorough, extensively researched, and well-written book, whose scope is not hindered by the abundance of case studies from Australian systems."

L. S. Rigg, Illinois University

Natural Areas Journal

"Technically-written...yet not overly technical considering the subject matter...several authors went to length to relate theory to practical restoration issues and problems. Details of case studies were relatable and complicated theoretical concepts were explained with top-notch flow charts, diagrams, and figures rather than esoteric mathematics...a lot of useful information."

"This is a very thorough, extensively researched, and well-written book, whose scope is not hindered by the abundance of case studies from Australian systems."

Product Details

Island Press
Publication date:
Science and Practice of Ecological Restoration Series
Edition description:
Product dimensions:
7.20(w) x 10.00(h) x 0.90(d)

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New Models for Ecosystem Dynamics and Restoration

By Richard J. Hobbs, Katharine N. Suding


Copyright © 2009 Island Press
All rights reserved.
ISBN: 978-1-61091-138-2


Models of Ecosystem Dynamics as Frameworks for Restoration Ecology


As rates of exotic species invasion, fragmentation, and climate change continue to accelerate, restoration faces increasingly greater challenges. Restoration must address the substantial, long-lasting reorganizations of ecosystems driven by these impacts. Among practitioners and scientists alike, there is increasing recognition that ecosystem dynamics can be complex, nonlinear, and often unpredictable (Wallington et al. 2005). Of particular importance is the recognition that some ecosystems may occur in a number of different states, which may be contingent on the history of disturbance, human intervention, or past restoration actions (Beisner et al. 2003; Suding et al. 2004). Complementary approaches using modifications of classical succession theory and the concept of assembly rules have also recently been investigated in the context of managing and restoring ecosystems (Young et al. 2001; Temperton et al. 2004; Hobbs et al. 2007). Adding further complexity, we better understand the importance of broad- scale processes and interactions between adjoining ecosystems; impacts in one place may be the result of events or management decisions elsewhere (Hobbs 2002). Taken together, these advances yield an exciting body of theory on which to rest restoration ecology (D'Antonio and Thomsen 2004; Hobbs and Norton 2004; Holl and Crone 2004; Young et al. 2005).

This book addresses how recent advances in our understanding of ecosystem and landscape dynamics can be translated into the conceptual and practical frameworks for restoration, adding to a number of excellent recent books developing linkages between ecological theory and restoration (Whisenant 1999; Walker and del Moral 2003; Temperton et al. 2004; Falk et al. 2006; van Andel and Aronson 2006; Walker et al. 2007). We explore how ecosystem models, particularly those that encompass nonlinear and complex dynamics, can be applied to the recovery of degraded systems (Hobbs and Norton 1996; Prober et al. 2002; Lindig-Cisneros et al. 2003; Suding et al. 2004). In this introductory chapter, we trace the development of these "new" ecosystem models in restoration, describe the main restoration approaches that would be taken based on the different type of ecosystem dynamics, and provide a synopsis of suitable evidence and approaches that can be used to determine what models of ecosystem dynamics may be applicable for particular systems and restoration situations. Lastly, we delineate the limitations and important considerations of this evidence that will affect inference, starting a discussion that continues in the contributed works that follow.

We also include a definitions of terms used throughout this book. Many terms have very precise definitions as they relate to ecosystem models. To avoid misperceptions and oversimplification, it is important to maintain very clear and unambiguous terminology as application of these models increase. We italicize our first use of terms throughout this chapter that we define in box 1.1.

Application of Ecosystem Dynamics to Restoration

Over the past 100 years, extensive work has documented how communities and ecosystems change in response to disturbance, external changes, or other types of perturbations. Despite the extensive documentation of patterns, there is no one agreed- on general conceptual framework concerning the controls on species turnover and ecosystem development, and new frameworks are still being proposed. Over the past decades, the focus of these models has expanded from assuming gradual continuum dynamics to models that incorporate alternative trajectories, thresholds, and stochasticity (fig. 1.1). This expansion has generated much interest in the synergistic potential among the models and restoration, as evidenced by the many recent reviews on the topic (Chapin et al. 2004; Mayer and Rietkerk 2004; Suding et al. 2004; Bestelmeyer 2006; Briske et al. 2006; Groffman et al. 2006; King and Hobbs 2006).

An early classic view that has been used to guide restoration was one of gradual linear change along a continuum (Clements 1916; Odum 1969; Pickett and McDonnell 1989), similar to succession toward a single climax state. One of the first conceptual models in restoration (Bradshaw 1984) depicted restoration as following this continuum model, hitting a single target restoration goal along a linear pathway (fig. 1.2). By understanding succession, this model implies that it is possible to predict, control, and perhaps accelerate community recovery after disturbances (Bradshaw 1987; Luken 1990; Dobson et al. 1997). It also assumes that there is an ultimate climax state that can be identified as the end point of the restoration effort. While a continuum approach has widespread appeal because it implies that we can predict and guide change in a system, it may presume that restoration efforts follow overly simple or unrealistic pathways in some cases (Lindig-Cisneros et al. 2003; Suding et al. 2004; Young et al. 2005; King and Hobbs 2006).

The idea that communities can develop into alternative stable states rather than into a single climax state was first proposed by Lewontin (1969). These models predict thresholddynamics, with a small change in environmental conditions causing an abrupt change in ecosystem function and/or community structure (fig. 1.1c). Because multiple states can exist given similar environmental conditions, feedbacks are important to maintain a system in particular state. Because of these feedbacks, the trajectory of shift from one state to another will differ from the trajectory required to return to the original state, leading to the possibility of an "irreversible collapse." While well documented theoretically, the existence of alternative stable states in ecological systems has met with much debate (Sutherland 1974; Connell and Sousa 1983; Grover and Lawton 1994). It has proven difficult to test for the existence of alternative equilibria empirically (Sutherland 1974; Connell and Sousa 1983; Grover and Lawton 1994; Petraitis and Latham 1999) because of criteria (e.g., stability, population turnover) that are often hard if not impossible to meet in natural systems. While there have been some recent successful demonstrations (see Schröder et al. 2005), rigorous tests of whether a degraded system truly represents an alternative and stable equilibrium are difficult and beyond the scope of most restoration efforts.

The importance of stochastic or nonequilibrium dynamics gained widespread acceptance throughout many branches of ecology in the 1970s and 1980s (Pickett et al. 1987; Luken 1990). Empirical evidence from a variety of systems has shown that disturbance type, biological legacies, and chance can create multiple trajectories and influence rates of change (Drury and Nisbet 1973; Coffin et al. 1996; Pickett et al. 2001). This perspective acknowledges that succession can be unpredictable with no tendencies toward any one permanent state (Zedler and Callaway 1999; Bartha et al. 2003; Benincà et al. 2008). It also acknowledges that stochastic dynamics influence the assembly of most species and that the degree to which stochastic forces influence assembly might be predictable based on environmental or regional processes (Chase 2003, 2007).

In addition, the early 2000s brought increased attention to regime shifts or dynamic regime models (Scheffer et al. 2001; Foley et al. 2003; Scheffer and Carpenter 2003; Collie et al. 2004; Mayer and Rietkerk 2004). Based on seminal work from Holling (1973), these models describe complex threshold dynamics with or without alternative states, avoiding much of the empirical difficulty associated with specific predictions of alternative stable state models and still describing many of the dynamics applicable to restoration, such as resilience (Gunderson 2000; Carpenter et al. 2001), adaptive capacity (Elmqvist et al. 2003), and feedbacks (Mayer and Rietkerk 2004) at multiple scales.

While the idea of alternative states was discounted for several decades in much of ecology, rangeland ecology adopted many of these ideas in the late 1980s in the form of state-and-transition models (fig. 1.3) (Westoby et al. 1989; Friedel 1991). A result of dissatisfaction with classic successional approaches to range condition assessment, state- and-transition models of rangeland vegetation dynamics split changes in rangeland systems into discrete states and describe processes that cause transitions between states (Bestelmeyer et al. 2003; Briske et al. 2003). For example, overgrazing enhances the survival of woody vegetation. Reduction of grazing intensity is not sufficient to restore the system to a healthy rangeland once this transition occurs (Friedel 1991); burning is needed to remove woody plants (Westoby et al. 1989). State-and-transition models have altered the general idea of rangeland management, refuting the general dogma that removing grazing from overgrazed rangeland is sufficient for recovery. Most state-and-transition models describe states, transitions, and thresholds largely qualitatively, although quantitative approaches are also possible (Allen-Diaz and Bartolome 1998; Jackson and Bartolome 2002). Rangeland ecology remains a leader in developing these ideas, with recent newer models proposed such as the spiral of degradation (fig. 1.3b) as well as the increased recognition of the importance of stochastic dynamics (Fynn and O'Connor 2000; Jackson and Bartolome 2002).

Shallow lakes are another system where these ideas of alternative states and thresholds gained credence in the late 1990s (Scheffer et al. 1997; Bachmann et al. 1999; van Nes et al. 2002). Lakes can exist in a state either where the water is clear and rooted plants are abundant or where the water is turbid and phytoplankton are abundant. In the clear lakes, rooted plants stabilize the sediment, reducing turbidity, and provide refuges for fish that eat phytoplankton. However, if the plants are removed or if fishing pressure is high, turbidity blocks light and resuspends sediment for phytoplankton, causing a rapid and dramatic shift (Moss et al. 1996; Carpenter et al. 1999). A turbid lake can be restored by manipulating the feedbacks that maintain the system in the turbid state: increasing the population of fish that consume phytoplankton, decreasing the number of predators that eat the phytoplankton-consuming fish, reducing nutrient loading, and installing wave barriers to create refuges for plants (Bachmann et al. 1999; Dent et al. 2002).

Recently, there has been an expansion of threshold and alternative state ideas to the general challenge of restoration of degraded land beyond rangeland and lake ecosystems. One conceptual model that has resonated widely is a two-threshold model where the first threshold denotes changed biotic interactions and the second, further down the degradation pathway, denotes changed abiotic limitations (fig. 1.4a; Hobbs and Harris 2001). Cramer and Hobbs (2005) modified this model by including multiple processes that interact to affect resilience and lead to degradation (fig. 1.4b). These and many other related conceptual models have been used as heuristic devices to guide restoration efforts and often prove to be consistent with land managers' perception of the restoration process (Wallington et al. 2005). We focus on this expansion of models in this book.

Ecosystem Models and Restoration Approaches

Ecosystem models can be crucial decision-making tools in restoration and land management. For instance, in restoration, it is important to recognize when ecological systems are likely to recover unaided and when they require active restoration efforts. This assessment involves the understanding (or estimation) of the recovery trajectory and identification, if any, of restoration thresholds that serve as barriers to prevent the recovery of degraded systems. These barriers can result from biotic factors (e.g., weed invasion, herbivory, lack of pollination) or abiotic factors (e.g., changes in hydrology or soil structure and processes) (fig. 1.4). Conceptual models of ecosystem dynamics can aid in this understanding and may reduce the risk of unpredicted or undesired change in restoration projects, suggesting ways to correctly diagnose ecosystem damage identify restoration constraints and develop corrective methodologies that aim to overcome such constraints.

While there are many types of ecosystem models and many ways to distinguish among the different types, three are particularly applicable to restoration ecology (table 1.1). First, continuum models describe dynamics without thresholds, where a change in the environmental controlling variable is more proportional to the system response. Second, stochastic models describe highly variable nonequilibrium relationships between system response and environment. These two types of models stand as alternatives to the third group, which constitutes the "new models" reference in the book title, threshold or regime shift models. These describe abrupt changes with small changes in environmental conditions. In the previous section, we described some of the historical developments related to these models; in this section, we describe the main restoration approaches that would be taken based on the different type of ecosystem dynamics.

Gradual Continuum Models

These models assume that systems respond in a continuous manner to environmental change and return to their predisturbance state or trajectory following disturbance (table 1.1). These models predict a classical successional trajectory: steady, directional change in composition to a single equilibrium point (Clements 1916; Odum 1969) with perturbations causing shifts along a common trajectory. Recovery is seen as a predictable consequence of interactions among species with different life histories and the development of ecosystem functions. Strong internal regulation occurs through negative feedback mechanisms, including competition and herbivore–predator interactions, as well as climate–ecosystem couplings and life history trade-offs.

In cases where these models apply to restoration, efforts can be designed from a perspective of initiating or assisting succession (Prach 2003; Sheley and Krueger-Mangold 2003). In some cases, community development can proceed spontaneously, relatively unassisted, to reach desirable target states (Prach et al. 2001; Khater et al. 2003; Novak and Prach 2003). In other cases, restoration can take this approach to spur recovery along a successional trajectory through the use of an understanding of positive and negative species interactions to either accelerate rates of change (Luken 1990; Choi and Wali 1995) or identify times when change is slowed and intervention is needed (Mullineaux et al. 2003). Restoration effort can then accelerate natural succession so that the ecosystem develops along the same trajectory as it would in the absence of intervention but reaches the goal end point sooner. For instance, prescribed burning of degraded grasslands can promote restoration, particularly if applied according to historical patterns (Baer et al. 2002; Copeland et al. 2002), and reinstating the original flow regime of a severely degraded river can spur recovery of the surrounding plant communities (Lytle and Poff 2004).

Stochastic Dynamics

Nonequilibrium theory assumes that external factors (e.g., climate, pollution) play a larger role in the behavior of ecosystems than do internal processes, such as competition and predation, and predicts divergent, cyclic, or arrested trajectories that never arrive at a common state. There are many examples that support this view. For instance, following the eruption of Mount St. Helens, in Washington, USA, there were variable rates of recovery along several distinct pathways (Franklin and MacMahon 2000) where chance colonization determined changes over time (del Moral 1998, 1999; Walker and del Moral 2003). In arid rangelands, large fluctuations in precipitation prevent herbivores from regulating primary production, thereby minimizing negative feedbacks that would cause equilibrium behavior (Ellis and Swift 1988; Jackson and Bartolome 2002). Stochastic effects resulting from isolation and dispersal limitations have also been shown to override the deterministic effects of competition (Underwood and Fairweather 1989; del Moral 1998; Foster et al. 1998). As seed limitation may be a very common barrier in degraded systems, stochasticity associated with dispersal may be important to consider in restoration projects (Young et al. 2005).


Excerpted from New Models for Ecosystem Dynamics and Restoration by Richard J. Hobbs, Katharine N. Suding. Copyright © 2009 Island Press. Excerpted by permission of ISLAND PRESS.
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