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University of Kiel, Ecology Centre, Msc Environmental Science, a seminar paper 
Status: completed (2009)

Ecosystem resilience
Caroline Bedeau and Ee-Ling Ng
caroline_903@hotmail.com, elng111@yahoo.com.my

 

Abstract

The concept of resilience is widely explored by ecological, political, social and institutional users and as such, various definitions of resilience exist. 3 descriptive definitions most relevant to ecology are explored here. Resilience is an emergent property of self-organised systems, arising from the interactions between patterns and processes. As a system is dynamic, a system’s structure and functions vary in time and space and as a result, its resilience varies too. This behaviour is described by the adaptive cycle theory developed by Holling in 1986. Two case studies of resilience in nature, one from a lowland dipterocarp rainforest in Malaysia and one from a coral reef ecosystem, are presented. The implications of resilience for ecosystem managers and some of the on-going efforts in ecosystem resilience research are discussed.

Content

1.    Defining resilience in ecology

2.    Resilience in self-organised systems

2.1.  Resilience and the adaptive cycle

2.2.  Resilience and ecosystem structure and function

2.2.1.     Biodiversity

2.2.2.     Energy flow and nutrient cycling

2.3.  Human influence on ecosystem resilience

2.4.  Case studies

2.4.1.     Case study 1: Resilience in terrestrial ecosystem

2.4.2.     Case study 2: Aquatic ecosystem resilience

3.    Implications for ecosystem managers

4.    Direction of resilience study

5.    References

1. Defining resilience in ecology

Resilience is a term that is used ubiquitously and at times, is a source of frustration for ecologists. It is explored by many scientific disciplines, with frequent refinement or even dilution to its definition, resulting in multiple definitions being used across the disciplines (e.g. Brand and Jax 2007; Webb 2007). The increase in the use of the concept of resilience among political, social and institutional users on one hand and decrease among ecologists on the other hand results in the dilution from a clear, well-defined concept favoured by ecologists to a normative perspective favoured by other user groups (Brand and Jax 2007). The 3 definitions presented here are of descriptive typology rather than normative to provide a clear, well-defined and specific concept that is most useful to ecologists.

The term ecological resilience was originally coined by Crawford S. Holling in 1973. He introduced resilience as the property of the system that “determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persist” (Holling 1973). This definition is also referred to as ecological resilience (Webb 2007).

Holling (1973) proposed that in a system subjected to high exogenous disturbances which therefore exists mostly in transient states, the constancy of the system’s behaviour is less significant than the persistence of relationships within the system. His definition, based on his observations on non-linear behaviour and inherent unpredictability in a system, assumes that there are multiple equilibria in a system and the tolerance of the system to perturbations facilitates transitions among regimes of behaviour by changing the variables and processes that control the behaviour.
 




Fig 1. Bowl heuristic of system stability (Holling 1973) developed based on prey-predator relationship. The depth of the bowl represents the amount of perturbation the system can absorb before changing basins of attraction. Lines represent possible trajectories. A slice of the bowl is removed for population Y to represent a population with lower extinction threshold. Shaded area represents the basin of attraction.

This concept of resilience started to influence other scientific disciplines by 1975 and forms the theoretical foundation of the current adaptive ecosystem management approach (Folke 2006). Another paradigm of resilience was developed by Stuart Pimm (1984) where he defined resilience as “the length of time that a system takes to return to equilibrium following perturbation”. This is also known asengineering resilience (Webb 2007). Pimm’s definition implies that there is a single basin of attraction, and therefore the length of time the system takes to return is a function of how far the system has moved from the equilibrium and how quickly it returns to the basin of attraction (Folke 2006). This paradigm is the basis of many earlier resource and ecosystem management strategies, whereby the focus is the “control of resource flows to maintain efficiency of function, constancy of system and predictable condition near a single steady state” (Folke 2006).
 




Fig 2. Ball and cup heuristic of system stability based on model developed by Scheffer et al. (1993) (Gunderson 2000). Valleys represent stability domains, balls represent the system, and arrows represent the disturbances. Engineering resilience (as defined by Pimm 1984) is determined by the slopes in the stability landscapes, whereas social-ecological resilience (or adaptive capacity) refers to the ability of the system to remain in a stability domain as the shape of the domain changes.

A third and more recent definition of resilience emphasises on adaptive capacity and the presence of dynamic basins of attraction (Folke 2006; Carpenter et al. 2001; Gunderson 2000). This is also known as social-ecological resilience (Carpenter et al. 2001). Such a system – (i) has the ability to recombine structures and processes through self-organisation, leading to renewal of the system and emergence of alternative basins of attraction and (ii) has the capacity to learn and adapt – is termed as complex adaptive system (CAS) (Folke 2006; Carpenter et al. 2001). Levin (1998) has described CAS as a system with emergent properties arising from localized interactions and autonomous selection processes acting at lower level; it is characterised by heterogeneity, non-linearity in development pathways, flows of matter, information and energy interconnecting various components and self-organised aggregation of components. Those who use this concept of a dynamic system with the capacity for renewal, re-organisation and system development have led the development of the concept of panarchy (Folke 2006, see Gunderson and Holling 2001 on panarchy).

Table 1. Characteristics, focus and context of resilience concepts ranging from a narrow interpretation to the broader social-ecological context (Folke 2006)

Resilience concepts

Characteristics

Focus on

Context

Engineering resilience

Return time, efficiency

Recovery, constancy

Vicinity of a stable equilibrium

Ecological/ecosystem resilience social resilience

Buffer capacity, withstand shock, maintain function

Persistence, robustness

Multiple equilibria, stability landscapes

Social-ecological resilience

Interplay disturbance and reorganization, sustaining and developing

Adaptive capacity, transformability, learning, innovation

Integrated system feedback, cross-scale dynamic interactions

All of these theorists essentially suggest the existence of a basin (or basins) of attraction. Kinzig et al.’s (2006) analysis of 4 regional examples of natural resource management found that besides cross-scale interactions within a domain that causes regime shifts, interaction between different domains means that a regime shift in one scale or domain may trigger cascading regime shifts. The authors argued that complexity and unpredictability are dominant characteristics of an ecosystem. From an ecological view point, these quasi-stable states suggest that resilience is an emergent property that allows a system to persist in particular basin (or basins) of attraction through continuous change. An alternative, anthropocentric viewpoint suggests that understanding an ecosystem’s response to disturbance enables us to pursue sustainability of ecosystem goods and services in an increasingly human-dominated landscape.

 The purpose of this paper is to explore the concept of ecosystem resilience, in particular its relevance to the field of ecology. First, we provide the background of how resilience occurs in self-organised systems. Next, we examine two case studies, one from a lowland dipterocarp rainforest in Malaysia and one from coral reef ecosystem. We discuss the implications of resilience for managers of those ecosystems. Then, we reiterate the general importance of resilience in ecosystem management. Finally, we describe some of the on-going efforts in ecosystem resilience research.

2. Resilience in self-organised systems
Common to all definitions of resilience is that resilience is an emergent property of ecological systems, and of self-organized systems in general, arising from the interactions between patterns and processes (Gunderson 2000). The concepts presented in this section are based upon Holling’s definition (1973) which recognizes the existence of multiple basins of attraction. It assumes that an ecosystem that returns to the same basin of attraction after a disturbance has high resilience. In contrast, if the structure and functioning of the basin of attraction change after the disturbance, the ecosystem is said to have low resilience. Thus, systems present different degrees of resilience, affected by their intrinsic properties. Additionally, resilience studies often view ecosystems as dynamic systems. Holling (1986) has proposed the adaptive cycle to represent the natural dynamics of ecosystems over time in a cyclical manner (see Carpenter et al. 2001; Gunderson 2000). The adaptive cycle theory suggests that a dynamic system does not tend toward any stable or equilibrium state, but passes through four typical phases of the adaptive cycle (Fig 3). Since the system structure and functions are changing along the phases, resilience as well is changing. In this part we explore how resilience evolves in a dynamic system and its relationship with ecosystem structure and function.

2.1. Resilience and the Adaptive Cycle
The growth or exploitation phase (r) corresponds to a phase of rapid colonisation of a disturbed area. This pioneer stage is characterised by high resilience mainly due to the adaptation of the biota to high environmental variability (r selection species) (Peterson 2009). As the system proceeds to the next phase, i.e. the conservation phase (K), a different set of species dominates, characterised by K selection; material and energy are stored and connectedness increases. Such an accumulation of capital is likely to fuel rapid structural changes and push the system into another basin of attraction. A collapse happens in the following phase, i.e. the release or creative destruction phase (Ώ). Disturbance agents (such as fire, insect pest) trigger the release of the accumulated capital and a breakdown in connections. After this event, the leftover capital starts to re-organise during the re-organisation phase (α) and provides the potential for subsequent growth, resource accumulation and storage. The stability region during this phase is wide and so the resilience is high. After this phase, the cycle continues with the r phase. The system resilience is low during the K and Ώ phases, and the ecosystem is at its most vulnerable state to move to another basin of attraction during these phases. However, more recently, Gunderson (2000) has argued that the resilience of the re-organisation phase is low due to the lack of local regulation and stability. We provide some examples below illustrating the relationship between resilience and the adaptive cycle.
        














Fig 3. The Adaptive cycle of  Ecosystems or Social Ecological  Systems. (The Resilience Alliance 2009).

In the lake ecosystems at the Great Lakes region of North America, two different stable states of the lake ecosystem exist: (i) a clear-water or oligotrophic state and (ii) a turbid-water or eutrophic state  (Fig 4); both are resilient (Carpenter et al. 1999 ; Scheffer et al. 2000 as cited in Carpenter et al. 2001). Carpenter et al. (1999) have conceived a theoretical model of the lake-agriculture social-ecological-system based upon the adaptive cycle (Fig 5). Ecological and social variables are changing over the cycle, as well as the resilience of the clear-water state. Hence, this resilience is high during the r phase, and decreases as the system moves into the K phase; reflected by the change in water quality from high to medium (Fig 5). If a disturbance, such as high input of nutrients, occurs during the K phase, the lake can shift into the turbid-water attractor. The Ώ phase is characterized by low water quality leading to social debates around agricultural methods. This is followed by the α phase where restoration measures and new approaches for agriculture are taken, which may lead to the recovery of the clear-water state. As social pressure on agriculture decreases, the cycle continues with a new r phase (Carpenter et al. 2001).

         




Fig 4. Resilience of the clear-water state of the lake ecosystem, with water phosphorus and sediment phosphorus concentrations (Carpenter et al. 2001). The solid curve represents the steady state of clear-water lake for the different combinations of the variables. In ∞ zone of the graph, resilience is infinite and for any conditions resulting from a perturbation, the system will end at a clear-water state. In the 0 zone of the graph, the lake will move into the turbid state whatever the initial conditions. However, in the middle zone, the unstable part of the curve delimits the fate of the system: if conditions after disturbance are above the unstable line, the resultant state will be turbid water; if they are below, clear water will be the resultant state. In this zone, the resilience of the clear lake is represented by the distance between the “clear” and the “unstable” steady states.




    



















Fig 5. Model adaptive cycle for the lake ecosystem in the Great Lakes district (Carpenter et al. 2001)
 

2.2 Resilience and Ecosystem Structure and Function

2.2.1 Biodiversity

The question of the relationship between biological diversity and resilience is a recent topic in resilience study, and often the approach is to explore the effects of species loss on ecosystem properties (Gunderson 2000; Petchey and Gaston 2009). For example, Di Falco and Chavas (2008) examined agroecosystem resilience in Southern Italy and found that higher crop diversity supports resilience to rainfall shocks by maintaining the system productivity. Different explanations have been proposed based on the niche and ecological function concepts.

The role of species richness in the stability of populations and ecosystems has been  continuously debated (e.g. Tilman 1996) particularly as few studies have been conducted about its role on resilience. A study by Peterson et al. (1998) indicates that the number of species, stability of ecological function and engineering resilience are linked; a higher richness leading to higher resilience. However, if the multi-stability domains view is considered, the relationship is not apparent (Engelhardt and Kadlec 2001; Peterson et al. 1998).

Diversity appears to be decisive for resilience through functional diversity (Peterson et al. 1998). The persistence of the different ecological functions is an important element of ecosystem resilience since the loss of a major function can cause dramatic alterations of overall ecosystem functioning (Folke et al. 2004). Two models address the relationship between functional diversity and resilience. Both recognise that redundancy of a function increases the ecosystem’s ability to face disturbance. The “rivet model” (Ehrlich and Ehrlich 1981) assumes that the ecological functions of different species overlap, so that even if one species is removed, the function persist until all the species performing the same function disappear. The “drivers and passengers” model (Walker 1992) assumes that functional groups, defined as groups of organisms that deliver the same ecological role (e.g. pollinators, grazers, nitrogen fixers), can be divided into two categories: drivers and passengers. Drivers are groups with a strong, important ecological function, whereas passengers have weak ecological impact. The presence or absence of the drivers determines the ecological resilience of the ecosystem. Thus resilience is affected by the diversity of the drivers, but as the classification changes with dynamic ecosystem conditions, the number of passengers that are potentially drivers is also an important determinant (Gunderson 2000).
An important distinction has to be made between the different spatio-temporal scales in which functional diversity applies. Within-scale, functional diversity represents the diversity of functions, whereas for a given ecological function, diversity operates mainly at different scales and enhances what Peterson et al. (1998) termed as cross-scale resilience (see Peterson et al. 1998 for example). Furthermore, the variability of the responses among species of the same functional group in face of environmental change is also critical for resilience (Folke et al. 2004). This factor is known as response diversity (Elmqvist et al. 2003). Genetic and population diversity are important factors determining the responses, and hence have indirect effects on ecosystem resilience.

Additionally, biodiversity has to be considered at larger spatial scale than the ecosystem scale, as well as the connectivity between areas; i.e essentially ecosystems must be examined within their landscape or regional context (Folke et al. 2004). Since mobile organisms connect areas, key species for ecosystem functioning can be recruited to a given place from the surroundings after a disturbance.

While most cases support that high diversity supports resilience, generalisation of this paradigm to all ecosystems is not possible. Petchey and Gaston (2009) found that in some instances, high levels of functional diversity due to high biodiversity are related with low resilience. The authors explain this by the presence of unique species which, when lost, cause a large reduction in functional diversity. This has important consequences for management since managing for high biodiversity can reduce resilience in these cases.

2.2.2 Energy flow and nutrient cycling

Besides studies examining the role of biodiversity for resilience, a number of theoretical and empirical studies have been carried out on the effect of nutrient cycling and energy flows on ecosystem stability and resilience (Loreau 1994). DeAngelis (1980) concluded that the resilience of an ecosystem increases when the energy flow per unit amount of energy within the community’s food web increases while for nutrient cycling, resilience decreases as the mean number of cycles performed by nutrients before to leave the ecosystem increases. However, Loreau (1994), using the same model as DeAngelis (1980) added that the more the system is close, the less the ecosystem is resilient, but that this is compensated by an increase in ecosystem resistance. In both studies, Pimm’s (1984) definition of resilience was used and resistance can be understood as the ability of the ecosystem to avoid the displacement out of the steady state. The problem induced by the lack of convergent definition is evident here. These results based on the perspective of a unique basin of attraction are not easily applicable to a multiple-equilibria perspective. This results in the difficulty to draw a broad picture of the determinants of ecosystem resilience.

2.3 Human Influence on Ecosystem Resilience

Many of the cases discussed above are impacted by human activities to various degrees. Humans have decreased the resilience of many ecosystems, making them more vulnerable to other disturbances (see examples by Gunderson 2000; Folke et al. 2004) and increase unpredictability in ecosystems’ capacity to provide ecosystem services. Such human-induced shifts in ecosystem states can be related to different types of changes (Fig 6, Folke et al. 2004): i) removals of entire functional groups, such as overexploitation of top-predators (top-down effects); ii) emissions of waste and pollutants, e.g through excess of nutrient inputs (bottom-up effects); iii) alterations of disturbance regimes to which the living community is adapted (e.g. fires) and iv) impacts of climate change.
    























Fig 6. Examples of ecosystem shifts due to human activities impacts, with alternate states and causes and triggers responsible for the loss of resilience and shift (Folke et al. 2004)



As previously mentioned, studies of resilience in ecosystems from terrestrial to aquatic are abundant (e.g. Newbery and Lingenfelder 2008; Grimsditch and Salm 2006; Carpenter et al. 2001; Walker et al. 1997; Scheffer et al. 1993). Below, we present in finer detail two such examples to further illustrate some of the concepts discussed above.

2.4 Case studies

2.4.1 Case study 1: Resilience in terrestrial ecosystem

This case study exemplifies how response diversity enhances resilience in an ecosystem. Newbery and Lingenfelder (2008) examined the plurality of tree species responses to drought perturbation in Bornean lowland dipterocarp rainforest as the basis the ecosystem’s resilience to severe climatic disturbance.
         




























Fig 7 . Study site in Danum Valley, Sabah.

Tropical rainforests are dynamic systems driven by regular perturbations on longer term scales. In Southeast Asia, droughts are dominant climatic variables that affect the rainforest growth. These droughts are associated with the El Nino Southern Oscillation cycle. Prior studies suggest that forests in parts of Borneo are at various stages of recovery from previous droughts dating as far back as 130 years ago. At Danum ValleyConservation Area, Sabah, Malaysia, a particularly severe drought was experienced in 1997/ 1998 (Fig. 8) creating deficit in soil water that affects the forest.

The study by Newbery and Lingenfelder (2008) recorded mortality, recruitment and stem growth rates for 34 common tree species at Danum in Sabah (Malaysian Borneo) in two 4-ha plots (trees ≥ 10 cm gbh) for two periods, 1986–1996 and 1996–2001. Mortality and growth were also recorded in a sample of 8 subplots for 22 small trees species (10 to <50 cm gbh) for two sub-periods, 1996–1999 and 1999–2001.

Fig 8 . Accumulated rainfall anomalies (ARA) with conditions applied at Danum, 1985–2003: running rainfall in 30 days (R 30) of mean daily (MDR; black line) and actual (ADR; red) rainfall, ARA 365 (blue) and accumulation only when R 30 < 232 mm (CARA 232; dark green); dashed reference at 100 mm (see text for explanations). Smoothing algorithm used a negative exponential function with sampling proportion equal to 0.02 (smoothing showed not all of the individual shorter and milder drought events as defined but was nevertheless preferred over the raw data for clarity). Intervals between plot measurements are shown as periods P1 and P2, and sub-periods P2a and P2b (Newbery and Lingenfelder 2008).


The effects of the drought were immediate observable on growth, recruitment and mortality as rainfall deficit over time limits soil water available to the trees. After the severe drought, annual mortality in the area increased by 45% and this effect increased negatively with tree size (Fig 9). As larger trees died more often than smaller ones, the canopy is exposed and understorey species benefits from increased light. In turn, they offer protection to canopy species’ saplings. The number of species that increased and decreased in annual recruitment are approximately equal (Fig 9), but the overall annual recruitment increased 12% after the drought. Two-third of the 34 species examined showed increased growth rate (rgr), with an overall increase of 12% recorded (Fig 9). However, examination of the subplots for small trees (10-50cm gbh) showed that the immediate response at drought was a decrease in rgr, particularly greater for the 40-50 gbh class but a high rgr during 1999 to 2001 recovery period led to higher rgr during and after the drought than before the drought (Fig 10).



         
Fig 9. Weighted percent changes between periods P1 (1986–1996) and P2 (1996–1999) in a mortality (m a, inverted scale), b recruitment (r a), c relative growth rate (rgr) (Newbery and Lingenfelder 2008).
Fig 10. Weighted percent change in relative growth rate between periods P1 (1986-1996) and sub-periods P2a (1996-1999); and between P2a and P2b (1999-2001) (Newbery and Lingenfelder 2008).
 
These results suggest that different species respond differently to drought, at varying degrees, at different times, with different extent of recovery and following different trajectories. These differences in responses are the result of differences in morphology and physiology (Gibbons and Newbery 2002 as cited by Newbery and Lingenfelder 2008). As drought events are stochastic, water deficit varies and the rainforest system experiences continuous immediate and lagged effects of perturbation. As such, these interspecific variations drive community dynamics and endow the system with resilience.

These results have implications for management strategies. Climate change scenarios predict changes in drought frequency and intensity. It is expected that higher mortality rates and longer periods of restricted growth would critically affect species recruitment and ultimately recovery of a species. These suggest that a change in forest structure and species composition may occur. As such rainforest management and conservation should be based on the existence of multiple basins of attraction.

2.4.2 Case study 2: Aquatic ecosystem resilience
Coral reefs are rich and complex ecosystems providing many ecosystem services. They are also among the most vulnerable ecosystems in the world, undergoing an important decline due to a wide range of disturbances, such as bleaching, fishing, pollution, invasive species (Grimsditch and Salm 2006 ; Nyström et al. 2008). Additional threats from climate change and other human pressures are expected to increase, leading to the development of studies concerned with coral reefs’ resilience (Nyström et al. 2008).

   
Fig 11. Example of a shift from a coral dominated ecosystem in the Caribbean region in the late 70's (left), and the same reef in the late 90's dominated by algal assemblages (right) (Hughes et al. 2005)(Photography by T.P. Hughes). Overfishing of herbivores (dominated by fishes on reefs) led to a switch in stable state from coral-dominated reef to algae-dominated reef. The decrease in fish populations allowed the increase in sea urchin populations, which became the key grazers. The coral-dominated state was maintained but it possessed a low resilience (due to decrease in the species diversity of the grazer functional group). High densities of urchin populations may have made them vulnerable to a disease that finally reduced the populations, thus decreasing herbivory pressure and leading to the development of the algae-dominated state.

This case study is focused on the resilience of coral reefs to bleaching, as analyzed by the IUCN Resilience Science Group (Grimsditch and Salm 2006). Bleaching is caused directly by the disruption of the symbiotic relationship between coral and algae, and leads potentially to the death of the reefs. Several disturbances can be the source of this interruption: freshwater flooding, pollution, sedimentation, disease and, most importantly, changes in light and temperature (Grimsditch and Salm 2006). After a disturbance, the direction of ecosystem development will depend on the sources of resilience present for the self-reorganisation. Grimsditch and Salm (2006) divided the parameters influencing ecosystem resilience into ecological and spatial factors, the former corresponding to properties within the ecosystem, while the latter correspond to agents beyond the ecosystem boundaries. Among ecological factors, species and functional diversity of specific groups hold an important role in determining reefs’ resilience. For example, a high diversity of grazers, comprising herbivorous fish and sea urchins, enhances resilience by decreasing algal growth and facilitating the settlement of coral propagules. Therefore they prevent the phase shift to algal-dominated reef. Additionally, mobile links are connecting habitats and transporting organisms between the different reefs. The species providing support for mobile organisms, such as sea grasses used as breeding ground, are also essential for high resilience. Another example is the predator group that plays a key role by maintaining a high diversity of herbivores and controlling eroder populations. For all these groups, within-scale diversity and cross-scale diversity must be considered in the monitoring of the reef ecosystem. Connectivity and reproduction are two related spatial factors. Connectivity among and within coral reefs plays an important role in ecosystem resilience since coral larvae being poor swimmers, water currents linking healthy reefs and damaged ones can help the recolonization and the maintain of genetic diversity in disturbed systems, thus increasing their resilience. The mode of reproduction of corals (asexual or sexual) is crucial in this point because it determines the distance of dispersal.

Based on this knowledge, the IUCN Resilience Science Group proposed some tools for managing coral reefs, including monitoring the ecological and socio-economic aspects of the ecosystem which enables the improvement of resilience by the identification and protection of the larval sources, connectivity patterns and important habitat types, and the management of threats. The relevance of the consideration of the socio-economic context besides the ecological system is stressed due to the interactions between these systems. Coral transplantation using juveniles from healthy ecosystems is also mentioned for reefs in degraded environment to enhance recovery, but this method is debated (Grimsditch and Salm 2006). To protect coral reefs from human disturbances and therefore increase their resilience, the setting up of protected areas (Marine Protected Areas) appears to be an effective strategy. They provide healthy communities necessary for the recovery of disturbed systems. Complementary to the protection of specific areas, the development of an integrated view of the landscape (or Integrated Coastal Management) is also necessary for an effective management of coral reefs resilience.


3. Implications for ecosystem managers

As shown in above case study examples, ecosystems are dynamic, evolving systems. In the context of socio-ecological systems (SES), there is increasing consensus that management should target to preserve or enhance resilience in the presence of anthropogenic influences. The concept of resilience as an economic asset has been gaining momentum (Vergano and Nunes 2006), further lending support to this concept of management for resilience. Humans have both top-down (e.g. resource over-exploitation compromise biological diversity’s contribution to system self-organisation ability) and bottom-up impacts (soil erosion, nutrient accumulation causing changes in ecosystem states) on the ecosystem (Gunderson and Pritchard 2002 as cited by Folke 2006; also see e.g. Leuteritz and Ekbia 2008). “Over the past 50 years, humans have changed ecosystems more rapidly and extensively than in any comparable period of time in human history, largely to meet rapidly growing demands for food, fresh water, timber, fiber, and fuel” (MEA 2005). These changes often enhanced vulnerability of such systems to further disturbances and compromise the provision of ecosystem goods and services that are fundamental to human wellbeing.



Fig 12. Relationship between management and ecosystem. Knowledge and understanding of ecosystem dynamics is the basis of managing ecosystems (Folke 2006)

In the presence of multiple basins of attraction and complex cross-scale interactions within the ecosystem as elucidated above, uncertainties and unpredictability are dominant characteristics of ecosystems. Management strategies need to consider intrinsic dynamics in combination with anthropogenic impacts on open ecosystems. These intrinsic dynamics may result in regime shifts in a system to a less desirable state with high resilience, thus undermining management as other thresholds are breached (Kinzig et al. 2006). These support an active adaptive management approach which provides flexibility to adjust and conform to dynamic systems while encouraging a systematic approach towards uncertainties. This requires the coupling of traditional scientific hypothesis development (but with an ecosystem-oriented goal in mind) with applied science of testing hypothesis through management actions (Gunderson 2000). And finally, the emphasis of management should be on preparedness rather than reactiveness (Folke 2006).


4. Direction of resilience study

A major challenge to the study of resilience is the harmonisation of the definitions of resilience and related concepts. The multitude of definitions and uses in research prevents the emergence of clear and convergent resilience study and management (Gibbs 2009). Carpenter et al. (2001) raised an important question of defining “resilience of what to what?”. However, Carpenter et al. focused only on one aspect of the system. The management of isolated aspects of a system may present unexpected negative repercussions on the others. Therefore, a normative view of resilience is important, and an effective balance between normative and descriptive resilience is needed. Even if the research is conducted at one focal scale, the system exists and functions at multiple spatial and time scales, and the interactions across scales are also of importance in determining the dynamics of the system (The Resilience Alliance 2009). As presented before in this paper, ecosystems are not isolated and must be considered in their landscape or regional context, and also in the human socio-economic context. Therefore, a good assessment and use of ecosystem resilience necessitates accounting the different domains, scales and interactions.

Another important area of research is the development of efficient surrogate indicators of resilience, instead of the development of models using attractor size (i.e. the size of the domain of attraction) (Carpenter et al. 2001). In the previous example of the Great Lakes ecosystem, the soil phosphorus content can be use as an indicator of clear-water state resilience.

Finally, an important feature of self-organized systems is their dynamism. Gunderson (2000) explained that the near-equilibrium view of ecosystems often leads to management failures. It is proposed that ecosystem management should focus on maintaining or increasing the resilience of the ecosystem to disturbance (e.g. Gunderson 2000; Grimsditch and Salm 2006). Therefore, two important considerations must arise when management undertakes the resilience approach:

  1. ecosystems are changing over time with changing environmental conditions in a natural process. The ecosystems we face today are not the same as thousands years ago. If management aims to keep a given system by enhancing its resilience, the system might become mal-adapted but still persist because of the high resilience. This shows that resilience is not an ideal in itself, since it can prevent adaptive change. According to Gunderson and Holling (2001), management must consider the resilience as dynamic and changing in order to generate a balance between vulnerability and persistence.
  2. as presented in the adaptive cycle section, resilience is high during the pioneer stage α. So targeting highly resilient ecosystem indicates maintaining the system to this stage and preventing the natural cycle from occurring. Moreover, some valuable ecosystems are in phases other than the exploitation phase, and thus present a normal condition of low resilience. To avoid these problems, resilience management as well as ecosystem management in general need to be flexible and adaptive.

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