Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances

Ecological Indicators 57 (2015) 395–408

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Review

Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances Takehiro Sasaki a,∗,1 , Takuya Furukawa b,1,2 , Yuichi Iwasaki c , Mayumi Seto d , Akira S. Mori b a

Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8563, Japan Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogaya, Yokohama 240-8501, Japan c Department of Civil Engineering, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8552, Japan d Department of Information and Computer Sciences, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan b

a r t i c l e

i n f o

Article history: Received 2 April 2014 Received in revised form 13 April 2015 Accepted 8 May 2015 Keywords: Alternative stable states Ecosystem services Ecosystem functioning Feedback mechanism Functional redundancy Irreversibility Press disturbance Pulse disturbance Response diversity Resilience

a b s t r a c t Ecosystem resilience is the inherent ability to absorb various disturbances and reorganize while undergoing state changes to maintain critical functions. When ecosystem resilience is sufficiently degraded by disturbances, ecosystem is exposed at high risk of shifting from a desirable state to an undesirable state. Ecological thresholds represent the points where even small changes in environmental conditions associated with disturbances lead to switch between ecosystem states. There is a growing body of empirical evidence for such state transitions caused by anthropogenic disturbances in a variety of ecosystems. However, fewer studies addressed the interaction of anthropogenic and natural disturbances that often force an ecosystem to cross a threshold which an anthropogenic disturbance or a natural disturbance alone would not have achieved. This fact highlights how little is known about ecosystem dynamics under uncertainties around multiple and stochastic disturbances. Here, we present two perspectives for providing a predictive scientific basis to the management and conservation of ecosystems against multiple and stochastic disturbances. The first is management of predictable anthropogenic disturbances to maintain a sufficient level of biodiversity for ensuring ecosystem resilience (i.e., resilience-based management). Several biological diversity elements appear to confer ecosystem resilience, such as functional redundancy, response diversity, a dominant species, a foundation species, or a keystone species. The greatest research challenge is to identify key elements of biodiversity conferring ecosystem resilience for each context and to examine how we can manage and conserve them. The second is the identification of ecological thresholds along existing or experimental disturbance gradients. This will facilitate the development of indicators of proximity to thresholds as well as the understanding of threshold mechanisms. The implementation of forewarning indicators will be critical particularly when resilience-based management fails. The ability to detect an ecological threshold along disturbance gradients should therefore be essential to establish a backstop for preventing the threshold from being crossed. These perspectives can take us beyond simply invoking the precautionary principle of conserving biodiversity to a predictive science that informs practical solutions to cope with uncertainties and ecological surprises in a changing world. © 2015 Elsevier Ltd. All rights reserved.

∗ Corresponding author. Tel.: +81 4 7136 4860; fax: +81 4 7136 4842. E-mail addresses: [email protected] (T. Sasaki), [email protected] (T. Furukawa), [email protected] (Y. Iwasaki), [email protected] (M. Seto), [email protected] (A.S. Mori). 1 These authors contributed equally to this work. 2 Present address: Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan. http://dx.doi.org/10.1016/j.ecolind.2015.05.019 1470-160X/© 2015 Elsevier Ltd. All rights reserved.

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Contents 1. 2. 3. 4. 5. 6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Disturbance types in ecosystems and their predictability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Categories of ecological thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Evidence for ecological thresholds: a summary of case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Perspectives for the management and conservation of ecosystems prone to multiple and stochastic disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Spatial and temporal perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Implications and concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406

1. Introduction Rapid global environmental changes such as those caused by human activities have considerable impacts on the ecological properties of ecosystems, and therefore on ecosystem functions and the services that human societies derive from them (Millennium Ecosystem Assessment, 2005). Because the responses of the ecosystem dynamics to such changes can be complex, nonlinear, and often unpredictable, ecosystem conservation and management efforts must deal with great uncertainty and surprise (Elmqvist et al., 2003; Folke et al., 2004). An ecological research priority is therefore to develop a management scheme that can secure essential ecosystem functions and services in highly stochastic environments. Ecosystem resilience is the inherent ability to absorb various disturbances and reorganize while undergoing state changes to maintain critical functions (Holling, 1973; Gunderson, 2000). When ecosystem resilience is sufficiently degraded or lost by disturbances, ecosystem is exposed at high risk of shifting from a desirable ecosystem state to an undesirable state. Ecological thresholds represent the points where even small changes in environmental conditions associated with disturbances leads to switch between ecosystem states (Suding and Hobbs, 2009; Standish et al., 2014). Characteristic mechanisms underlying threshold occurrence are changes in biotic/abiotic interactions and feedbacks. Disturbances are assumed to induce a state shift by modifying biotic interactions and feedbacks within communities such as competitive dynamics and plant–herbivore interactions (Briske et al., 2006; Suding and Hobbs, 2009). As the degradation trajectory goes through, thresholds may be surpassed in response to abiotic changes that modify site characteristics such as climate change, severe soil erosion, and eutrophication, resulting in the further degradation of ecosystem state. Studies of a broad range of ecosystem types have provided evidence for ecological thresholds in state changes (Scheffer and Carpenter, 2003; Mumby et al., 2007; Sasaki et al., 2008; Furukawa et al., 2011). Typical examples include observations that human-induced eutrophication leads to shifts from clear-water to turbid-water state in lake ecosystems (Scheffer and Carpenter, 2003) and that severe grazing leads to shifts in vegetation state from dominance by perennial grasses and forbs toward dominance by unpalatable forbs and weedy annuals (Sasaki et al., 2008). Such regime shifts imply shifts in important ecosystem functions and services of an ecosystem (Carpenter et al., 2001; Folke et al., 2004). Since regime shifts would often necessitate costly restoration efforts, local management practice should primarily aim to prevent them (Briske et al., 2006; Suding and Hobbs, 2009). Many studies on ecological thresholds primarily consider an anthropogenic disturbance prevailing in a given ecosystem. However, anthropogenic disturbances are likely to be interact with multiple natural disturbances that occur stochastically and are thereby unpredictable at different spatio-temporal scales. For example, climatic events and prolonged natural disturbances, which can interact with anthropogenic disturbances to modify

ecosystem properties such as nutrient cycling and decomposition processes (Smith et al., 2009; Collins et al., 2011). In this context, conceptual models such as state-and-transition models represent as a useful framework to accommodate a broad spectrum of potential state transitions along various disturbance gradients (Briske et al., 2005, 2006). Ecological thresholds can be used to differentiate among various stable states that are likely to exist within ecosystems. State-and-transition models provide opportunities to achieve favorable transitions and hazards to avoid unfavorable transitions between ecosystem states (Briske et al., 2005, 2006). Due to highly unpredictable nature of ecosystem dynamics against multiple and stochastic disturbances, however, such conceptual models have been applied to management and restoration largely heuristically with incomplete ecological knowledge (Briske et al., 2008; Suding and Hobbs, 2009). Two perspectives can be proposed to provide a predictive scientific basis to management prescriptions involved in conceptual models for avoiding state transitions caused by multiple and stochastic disturbances. The first perspective is to examine how a particular element of biological diversity confers resilience and to actively manage and conserve it, that is, resilience-based management (Briske et al., 2008; Suding and Hobbs, 2009). Several biological diversity elements appear to play a key role in ecosystem resilience, including functional redundancy, response diversity, a dominant species or foundation species, and a keystone species. Functional redundancy is the number of species contributing in a similar way to an ecosystem function (Laliberté et al., 2010). Response diversity refers to the existence of various responses to similar environmental change among species constituting a certain ecosystem function (Elmqvist et al., 2003; Mori et al., 2013). Several studies suggested the importance of these indices in conferring ecosystem resilience (Laliberté et al., 2010; Chillo et al., 2011; Pillar et al., 2013). However, disturbances alter not only species diversity itself but also community dominance and membership in most ecosystems (Hillebrand et al., 2008). When dominant or foundation species contributes most to ecosystem functions and stability, their disproportionate loss by disturbances such as selective harvesting or pathogen outbreaks would greatly reduce ecosystem resilience (Grman et al., 2010; Sasaki and Lauenroth, 2011). Regardless of species diversity, loss of a potential keystone species may have cascading effects to community structure that can ultimately erode ecosystem resilience (Power et al., 1996). Thus, it is important to identify a key element of biological diversity conferring ecosystem resilience for each of ecological and environmental contexts. The second is to identify ecological thresholds along existing or experimental disturbance gradients (Briske et al., 2008; Suding and Hobbs, 2009). Although this approach is a posteriori evaluation of state transitions which are sometimes irreversible in a given locality, the identification of thresholds would facilitate the understanding of threshold mechanisms as well as the development of indicators of proximity to thresholds for other localities. The implementation of forewarning indicators will be critical particularly when

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resilience-based management fails. The ability to detect an ecological threshold along disturbance gradients should therefore be essential to establish a backstop for preventing the threshold from being crossed. Although previous syntheses have claimed the importance of such perspectives (Briske et al., 2008; Suding and Hobbs, 2009; Standish et al., 2014), we need to organize these perspectives in a more realistic applied context for providing a predictive scientific basis to better management and conservation of ecosystems prone to multiple and unpredictable disturbances. In particular, we argue that it is time to move beyond simply invoking the precautionary principle of conserving biodiversity toward a predictive science that identifies specific elements of biological diversity conferring ecosystem resilience, thereby informing practical solutions to cope with uncertainties and ecological surprises. We first define different types of disturbances to formalize the contexts where the perspectives can be utilized in ecosystem management. Then, we summarize the recent literature on ecological thresholds to clarify the present status and the limitations of current knowledge. Finally, we explain how the perspectives can be applied when facing multiple and stochastic disturbances, and we suggest future ways in which the concepts of resilience and thresholds might be implemented by ecosystem managers in a changing world. 2. Disturbance types in ecosystems and their predictability In this paper, the definition of a disturbance follows Pickett and White (1985) with slight modification, as any detectable alteration of an ecosystem, community, or population structure, or of resources, substrate availability, or the physical environment caused by external factors. Disturbances can be broadly categorized into anthropogenic and natural disturbances (Table 1). They can further be classified into a press and pulse category (Table 1). A disturbance that occurs as a relatively discrete event in time is referred to as a pulse disturbance, while more gradual or cumulative pressure on an ecosystem is referred to as a press disturbance (Resilience Alliance, 2010). Disturbance regimes can change over time and have inherent degree of uncertainty (Resilience Alliance, 2010). In general, many anthropogenic disturbances are press disturbances that occur within the decision-making timeframe, and they can be more predictable with regard to their ecological consequences than natural disturbances (Collins et al., 2011; Tomimatsu et al., 2013). Exceptionally, ecological consequences of prolonged anthropogenic press disturbances such as global warming are difficult to predict due to uncertainties as to ecosystem trajectories under

397

future global changes (Shaver et al., 2000; Botkin et al., 2007; Dawson et al., 2011). Some anthropogenic disturbances such as clear logging are instantaneous pulse disturbances, but in principle such disturbances can also be predictable in that human intends to drive them. However, human-influenced ecosystems are also likely to be affected by multiple natural disturbances that occur stochastically at different spatio-temporal scales and which, for that reason, might be difficult to predict (Collins et al., 2011; Peters et al., 2011). For example, it is usually difficult to predict ecological consequences of extreme climatic events such as hurricanes or droughts (Scheffer et al., 2001). Ecological consequences of prolonged natural press disturbances such as naturally induced climate change, geothermal discharge or sedimentation on ecosystem states are also less predictable, in that it is difficult to understand ecological dynamics under unmeasured or novel environments for time periods outside the realm of modern observations (Williams and Jackson, 2007; Dawson et al., 2011; Standish et al., 2014). Combination of multiple and stochastic disturbances and their timing can cause interaction effects on ecosystem states. For example, an additional natural disturbance will force an ecosystem to cross a threshold that an anthropogenic disturbance alone would not have achieved (Scheffer and Carpenter, 2003; Mumby et al., ˜ 2007). By contrast, the dramatic increase in rainfall during El Nino events can facilitate the restoration of degraded arid ecosystems by adjusting the level of grazing intensity (Holmgren and Scheffer, 2001). To avoid negative state transitions and facilitate positive state transitions under uncertainties around multiple and stochastic disturbances, the development of resilience-based management as well as the identification of ecological thresholds should be ecological research priorities (see the latter section “Perspectives for the management and conservation of ecosystems prone to multiple and stochastic disturbances”). 3. Categories of ecological thresholds Ecological thresholds can be categorized into two groups according to the progression of threshold development (Briske et al., 2005, 2006). The first is a structural threshold based on changes in ecosystem structure such as species composition, functional group composition, and the presence of invasive species (Briske et al., 2005, 2006). Thresholds in community composition shifts from dominance by perennial grasses and forbs toward dominance by unpalatable forbs and weedy annuals along a gradient in grazing intensity are one of widely recognized structural thresholds in rangeland ecosystems (Sasaki et al., 2008). Structural thresholds has received the greatest attention because it can be most

Table 1 Disturbance types in ecosystems and their predictability with regard to their ecological consequences. Press–pulse classifications

Typical examples

Time frame

Predictability

Anthropogenic disturbances

Natural disturbances

Press-type

Pulse-type

Press-type

Pulse-type

Land-use change, grazing, eutrophication, global warminga Short-term (generally within the time frame of human decision-making) Predictable

Clear logging, temporary pollution

Geothermal discharge; sedimentation; climate change Prolonged

Hurricane, fire, flooding, drought, heavy rainfall Instantaneous

Less predictable

Less predicable

Instantaneous

Predictable

a Although many anthropogenic disturbances are predictable, ecological consequences of prolonged anthropogenic press disturbances such as global warming are difficult to predict due to uncertainties as to ecosystem trajectories under future global changes.

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easily observed and quantified, and it will more readily translate into management guidelines (Briske et al., 2006). The second is a functional threshold that is exceeded when ecosystem structure is sufficiently modified to accelerate ecological processes degrading ecosystem states (Briske et al., 2005, 2006). A typical example of functional threshold is that sufficient change in vegetation cover by disturbances such as intense grazing and climate change can accelerate the rate of soil erosion, which further degrades vegetation states and in turn increases erosion rates in arid ecosystems (Chartier and Rostagno, 2006; Gao et al., 2011; Munson et al., 2011). This positive feedback mechanism maintains or reinforces the degraded vegetation states and constrains reversal to the previous states (Scheffer and Carpenter, 2003; Suding et al., 2004). Once a functional threshold has been exceeded, active restoration efforts are required to disrupt positive feedbacks and reestablish ecological processes that maintain desirable states (Suding et al., 2004; Briske et al., 2006). The potential for threshold reversibility is constrained by the extent and duration of ecosystem modifications, and the strength of feedbacks (Suding et al., 2004; Briske et al., 2006). If there are no such recovery constraints, threshold changes in one direction could be reversible and result in a sudden recovery in the opposite direction (Groffman et al., 2006; Suding and Hobbs, 2009). In contrast, in some cases, threshold changes are reversible but the recovery pathway to the original state is different from the degradation pathway (Suding et al., 2004; Groffman et al., 2006; Suding and Hobbs, 2009). This hysteresis response could occur when there are two or more alternative stable states for one given external environmental condition (Groffman et al., 2006; Suding and Hobbs, 2009). Further, when the properties of original state have become completely extinct and the strength of feedback is sufficiently strong to reinforce the degraded states, threshold changes could not be reversed at all (Suding et al., 2004; Briske et al., 2006). Unnecessary human interventions, particularly when threshold changes are reversible without hysteresis, would impose additional costs on ecological management and restoration efforts. A risk of false detection of either threshold reversibility or irreversibility could be minimized when they are evaluated over broad spatial and temporal scales (Bestelmeyer et al., 2013). Thus, it is critical for managers to make some evaluation of potential reversibility of threshold changes in ecosystem states before establishing a program of management or restoration (Groffman et al., 2006; Briske et al., 2008).

4. Evidence for ecological thresholds: a summary of case studies To evaluate the current understanding of nonlinear ecosystem responses to disturbances, we conducted a review based on publications found through an ISI Web of Science search (conducted on March 26, 2013 for articles published after 1979) for the search term “ecological threshold(s)” in the title, abstract, or keywords. Of the 246 articles found by this search, 147 used or mentioned the use of quantitative methods to identify an ecological threshold (see Appendix). We found that nearly half of the past findings were derived from studies conducted in North America, while studies in Africa, Latin America, and Oceania were limited (Fig. 1a). The target ecosystem also varied by region as forest, river and wetland were popular in North America, lake and marine in Europe, and grassland in Asia (Fig. 1a). Other ecosystems are listed in Table 2. Long-term (≥10 years) studies were mostly conducted in forest, lake/pond and marine ecosystems (Fig. 1b) probably due to the availability of longterm data such as tree ring records, sediment records, and fishery statistics. Threshold responses were examined mainly in relatively simple settings where 73.5% of the studies targeted a single disturbance

predominantly under an anthropogenic setting. Additionally, regardless of the number of disturbance covered, most anthropogenic disturbances were press-type (Fig. 1c) prevailing in each ecosystem, except for global warming (Table 2). None of the studies in river dealt with a pulse disturbance (Table 2), which might be explained by the heavy reliance on snapshot data (Fig. 1b). Ecosystem response becomes complex and often highly unpredictable when multiple disturbances with different spatiotemporal scales (i.e., press–pulse disturbances) interact (Collins et al., 2011). However, among the 147 studies, only 13 examined press and pulse disturbances at the same time (Fig. 1d). One study by Li et al. (2007) investigated anthropogenic press–pulse interaction in an experimental setting: the authors found that combinations of soil amendment, cultivation, grazing exclusion and prescribed fire were not able to revert a degraded grassland to a pre-historic state in the short term (2 years) probably due to the loss of native species from the local flora. The consequences of multiple natural disturbances were investigated using longterm data such as pollen/sediment records (Gillson and Ekblom, 2009) or were inferred from data using space-to-time substitutions (e.g., Azihou et al., 2013). The complex interaction of natural press–pulse disturbances was highlighted in a study on grasslandsavanna transition (Gillson and Ekblom, 2009) in which a natural press disturbance (i.e., grazing by wild herbivores) functioned as a negative feedback in the grassland state, while a natural pulse disturbance (i.e., fire) changed its role from a negative to positive feedback as the grassland to savanna transition took place. Although limited in number, many of the press–pulse interactions examined in past literature addressed the combination of anthropogenic and natural disturbances (Fig. 1c). Among these studies, the combination of a natural pulse disturbance and an anthropogenic press disturbance was popular. In the case of a boreal forest, human-induced global warming not only directly affected forest conditions, but also altered the natural disturbance regime by increasing the frequency and magnitude of fires, which effect was predicted to exceed the potential negative feedback caused by the increase in less flammable species during the forest transition (Mann et al., 2012). Studies also indicated that natural pulse disturbance can move the level of ecological threshold along an anthropogenic press disturbance. For instance, even a sustainable fishing scenario was predicted to be jeopardized by an ˜ year in a study of the coast of Chile (Neira unproductive El Nino et al., 2009). Conversely, natural pulse disturbances that create resource pulses could be used as temporal windows of opportunity for restoration because a small reduction in anthropogenic press disturbance could be sufficient to induce recovery in a productive regime (Holmgren and Scheffer, 2001; Neira et al., 2009; Letnic and Dickman, 2010). In such situation, the ecological threshold and underlying mechanisms must be well understood to decide whether it is worthwhile to wait for a pulse event to assist system recovery (Holmgren and Scheffer, 2001). When consequences of interacting disturbances are not well understood, resilience-based management is one of the recommended management prescriptions (Briske et al., 2008; Suding and Hobbs, 2009). As discussed, ecosystem resilience is conferred through elements of biological diversity (e.g., functional redundancy, response diversity, dominant/foundation species, and keystone species) which also determines the structure and composition of the ecosystem. To understand the basis of what we know of ecosystems exhibiting threshold responses, we grouped the ecosystem properties (i.e., response variables) investigated in the literature into six categories: population measures (e.g., recruitment, survival and extinction rates, single species abundance), diversity measures (e.g., species richness, diversity indices), community structure (e.g., composition, trophic/guild structure),

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399

Fig. 1. The characteristics of past literature dealing with ecological thresholds (those that included the keyword “ecological threshold(s)” in their title, abstract, or keywords) shown by the number of articles: (a) type of ecosystem summarized by region (including double-count for multi-regional studies); (b) observation time scale summarized by ecosystem type; (c) disturbance types (press/pulse) summarized by disturbance cause; (d) response variables summarized by disturbance types (press/pulse); (e) observation time scale (studies considering multiple variables were counted for each variable). ‘Other’ region includes studies covering the entire planet, the Arctic, lab experiments and simulation models replicating an ecosystem without any specific geographic reference, and meta-analyses; ‘other’ observation time scale includes simulation models without explicit time frame; and ‘other’ disturbance cause includes natural environmental gradients (e.g., altitude) and those without specific disturbance/environmental gradients.

ecosystem function (e.g., productivity, respiration), ecosystem state (e.g., phases represented by different physiognomy, community type), and other/unspecified. We found that the most popular ecosystem property was community structure and composition, while the use of biodiversity measures has been limited and has concentrated on press-type disturbances (Fig. 1d). This might indicate the limited knowledge on the strength of biological diversity indices contributing to ecosystem resilience under multiple disturbances of different spatio-temporal scales. Another concern

was that the use of biodiversity measures was largely limited to snapshot studies (Fig. 1e). Considering relaxation time (Brooks et al., 2002) and hysteresis (e.g., Isbell et al., 2013) of species responses to perturbation, long-term research on the role of biodiversity in the context of ecological threshold could become increasingly important. Not many studies directly addressed the feedback mechanisms underlying threshold responses, but in certain ecosystems some well-recognized processes were identified. For example,

400 Table 2 Disturbances covered in the literature according to ecosystem and disturbance type. The numbers in parentheses indicate the number of articles. Note that anthropogenic global warming and naturally induced climate change were differentiated based on the historical length of the disturbance (i.e., categorized as global warming if less than ca. 300 years) if no other description was provided. Ecosystem type

Anthropogenic disturbance

Natural disturbance Pulse-type

Press-type

Pulse-type

Forest/Woodland (42)

Fragmentation/deforestation Logging Vegetation management Global warming Fire suppression Species invasion

Logging

Climate change Herbivory

Fire

River/Stream (24)

Land-use change Eutrophication Chronic pollution Species invasion

–

Geothermal discharge Sedimentation

–

Lake/Pond (20)

Eutrophication Global warming Acidification Land-use change Chronic pollution Water extraction Species invasion

–

Climate change

Extreme climate event

Grassland/Rangeland (18)

Grazing Vegetation management

Prescribed fire

Climate change

Extreme climate event

Wetland (15)

Eutrophication

Prescribed fire

Climate change

Heavy rainfall event (short-term)

Land-use change Chronic pollution Water management

Logging

Fishing/harvesting Global warming Eutrophication

–

Climate change

Extreme climate event

Mining; Physical alteration; Global warming Landscape alteration Eutrophication; Land-use change; Human population increase

Prescribed fire –

Herbivory; Soil erosion –

Fire –

– –

– Climate change; Predation

– Fire

Marine (13)

Others (15) Forest-savanna (5) Soil (3) Agricultural landscape (2) Aquifer; Earth; Habitat model; Intertidal zone; Shrubland (1 each)

T. Sasaki et al. / Ecological Indicators 57 (2015) 395–408

Press-type

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in arid rangeland ecosystems, studies considered vegetation–soil interactions (e.g., decrease in vegetation cover leading to increase in soil erosion, and vice versa; Schlesinger et al., 1990) to play an important role in driving the ecosystem across a threshold (Chartier and Rostagno, 2006; Sasaki et al., 2008) and in preventing its recovery (Gao et al., 2011; Sasaki et al., 2013). Other suggested feedback mechanisms were interactions between species traits and fire frequency underlying the alternating shifts between forest and non-forest states (e.g., Martin and Kirkman, 2009; Halpern et al., 2012; Hoffmann et al., 2012), and alteration of ecosystem properties by species invasion (e.g., Furukawa et al., 2011). However, the mediation of nonlinear ecosystem responses to multiple and stochastic disturbances by such feedback mechanisms is difficult to demonstrate, and remains poorly understood. Furthermore, examples of direct intervention of feedback mechanisms for ecosystem restoration was limited to few studies (e.g., Martin and Kirkman, 2009; Halpern et al., 2012).

(Mumby et al., 2007) in which the positive feedback between the decreased grazing rate and the increased dominance of macroalgae inhibited coral recruitment and growth (Mumby et al., 2007; Mumby and Steneck, 2008). This example illustrates how an ecosystem state can easily be tipped from healthy to unhealthy if ecosystem resilience (in this case, conferred by key algal grazers) has been sufficiently attenuated by a sustained anthropogenic disturbance. When facing uncertainties of ecosystem dynamics caused by multiple and stochastic disturbances, two perspectives can be proposed to provide a predictive scientific basis to ecosystem management: (1) active management of ecosystem resilience and (2) the identification of an ecological threshold along disturbance gradients. The first is management of predictable anthropogenic disturbances (Fig. 2a) to maintain a sufficient level of biodiversity for ensuring ecosystem resilience against less predictable natural disturbances (Fig. 2b and c). The adverse impacts of natural disturbances (Fig. 2b and c) as well as anthropogenic disturbances (Fig. 2a) can then be mitigated by enhanced resilience and an ecosystem cannot be forced to cross an ecological threshold. Several biological diversity elements appear to confer ecosystem resilience, such as functional redundancy (Chillo et al., 2011; Reich et al., 2012; Pillar et al., 2013), response diversity (Elmqvist et al., 2003; Mori et al., 2013), a dominant species (Grman et al., 2010; Sasaki and Lauenroth, 2011), a foundation species (Martin and Goebel, 2013), or a keystone species (Menge et al., 1994; Power et al., 1996). Recently, functional redundancy and response diversity have frequently used as a proxy measure of resilience (Laliberté et al., 2010; Chillo et al., 2011; Pillar et al., 2013; Standish et al., 2014). Chillo et al. (2011) examined the effect of response diversity quantified as diversity of response traits within each functional group on ecosystem resilience in the semi-arid grassland of Argentina and found that response diversity is important to determine resilience to grazing impacts. Pillar et al. (2013) quantified functional redundancy as the difference between species diversity and functional diversity and demonstrated that functional redundancy enhance resilience to grazing impacts. Functional redundancy and response diversity would thus be useful as proxy measures of ecosystem resilience (Standish et al., 2014). However, Mori et al. (2013) suggested that

5. Perspectives for the management and conservation of ecosystems prone to multiple and stochastic disturbances Intense anthropogenic disturbance (Fig. 2a) significantly reduces ecosystem resilience and often induces a shift in ecosystem state from desirable one to undesirable one. There is a growing body of empirical evidence for such state transitions caused by anthropogenic disturbances in a variety of ecosystems (see previous section). However, fewer studies addressed the interaction of anthropogenic and natural disturbances (Fig. 2b) that often force an ecosystem to cross a threshold which an anthropogenic disturbance or a natural disturbance alone would not have achieved (Scheffer and Carpenter, 2003; Mumby et al., 2007). For example, a natural pulse disturbance such as a hurricane could substantially reduce coral cover, but the presence of sea urchins and parrotfishes, which are key grazers on macroalgae, could prevent coral reefs from being covered by algae (Hughes, 1994; Mumby et al., 2007). Where sea urchins and parrotfishes had been overexploited, a hurricane killed many corals and created colonizing space for macroalgae, thus driving the reefs on a negative trajectory toward algal domination

Anthropogenic disturbances (predictable)

401

Natural disturbances (less predictable)

(b)

(a)

(c) Ecosystem state

Biological diversity e.g., Funconal redundancy, Response diversity, Dominant species, Keystone species

Structure and composion e.g., Species composion, Funconal group composion

Ecosystem funcons and services Fig. 2. Pathways connecting anthropogenic and natural disturbances with ecosystem functions and services. Intense anthropogenic disturbances significantly affect ecosystem states and reduce ecosystem resilience (a), often resulting in a shift in ecosystem state from desirable one to undesirable one. Anthropogenic disturbances are likely to be interacted with natural disturbances (b) to affect ecosystem states. Natural disturbances also affect ecosystem states directly (c). To avoid negative consequences of multiple and stochastic disturbances on ecosystem functions and services, the development of resilience-based management as well as the identification of ecological thresholds should be ecological research priorities (see text).

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high functional redundancy does not always ensure high response diversity, and thereby ecosystem resilience. Complementary use of functional redundancy and response diversity should be considered when used as surrogates for resilience. Disturbances alter not only diversity itself but also community dominance and membership in most ecosystems (Hillebrand et al., 2008). There is a growing concern on the role of dominant species maintaining ecosystem resilience against multiple and stochastic disturbances (Smith and Knapp, 2003; Grman et al., 2010; Sasaki and Lauenroth, 2011). Sasaki and Lauenroth (2011) manipulated community dominance in the shortgrass steppe and suggested that dominant species contributes most to ecosystem stability and resilience against livestock grazing and high interannual variability in rainfall. In real ecosystems, the abundance distributions of species in ecosystems are greatly skewed, but if the dominant species is resilient to disturbances, or if it provides structure to the community by creating locally stable conditions for other species (i.e., foundation species), then the relative importance of dominant species in ecosystem resilience would be high (Smith and Knapp, 2003; Hillebrand et al., 2008; Grman et al., 2010; Sasaki and Lauenroth, 2011). Loss of key or keystone species that have cascading effects to community structure also erode ecosystem resilience as is the case for shifts from coral-dominated to algal-dominated state (see the first paragraph of this section). Key elements of biological diversity conferring ecosystem resilience would thus be different among ecological and environmental contexts. Moreover, other elements of biodiversity such as spatial distribution or connectivity of species and communities would be more important at larger scales

(Standish et al., 2014). The greatest research challenge is to identify key elements of biodiversity conferring ecosystem resilience for each context and to examine how we can manage and conserve them. The second is the identification of ecological thresholds along existing or experimental disturbance gradients (Briske et al., 2008; Suding and Hobbs, 2009). This will facilitate the development of indicators of proximity to thresholds as well as the understanding of threshold mechanisms. Sasaki et al. (2011) identified the indicator species and functional groups to predict the proximity of ecological thresholds along grazing gradients in Mongolian grasslands. Such forewarning indicators of ecological thresholds can be easily recognized and implemented in local management practice at some other threshold-prone sites. Gao et al. (2011) used long-term monitoring data for vegetation recovery, and revealed that the sites with below 20% vegetation cover cannot recover even after three decades of the cessation of tree harvesting and domestic grazing. They indicated that the rate of soil erosion was accelerated when vegetation cover decreased below 20%, which further reduced vegetation cover. In this case, ecological threshold at about 20% vegetation cover can be used as a benchmark to avoid positive feedback between soil erosion and vegetation cover (Gao et al., 2011). However, such ecological thresholds do not necessarily have high precision and sometimes move in response to multiple and stochastic disturbances (Folke et al., 2002; Standish et al., 2014). Therefore, the forewarning indicators of ecological thresholds should be used as a backstop for when resilience-based management fails.

Ecosystem state

Key biodiversity element

(a) Management of anthropogenic disturbances to (b) Idenficaon of ecological thresholds opmize the levels of key biodiversity elements to develop forewarning indicators of state transions 1) 2) 1) 2)

4)

3)

Anthropogenic and/or natural disturbances

Anthropogenic disturbance (a)

Exposure to mulple and stochasc disturbances

3)

(b)

(b)

Undesirable state

Desirable state

Degradaon trajectory Fig. 3. Two perspectives to provide a predictive scientific basis to ecosystem management in the face of multiple and stochastic disturbances: (a) management of anthropogenic disturbance to optimize the levels of key biodiversity elements conferring ecosystem resilience and (b) identification of ecological thresholds to develop forewarning indicators of state transitions. (a) We would expect several typical response patterns of key biodiversity elements to anthropogenic disturbances: (1) negative monotonic, (2) unimodal, (3) neutral and (4) positive monotonic. Anthropogenic disturbances should be managed according to these response patterns and their underlying factors so as to reinforce ecosystem resilience (i.e., resilience-based management). (b) Possible threshold relationships between ecosystem states and disturbances: (1) reversible without hysteresis, (2) reversible with hysteresis, and (3) irreversible. Developing forewarning indicators of ecological thresholds (indicated by the dotted vertical lines) is fundamental so as to provide a backstop for resilience-based management. In the ball and cup diagrams, the balls represent the ecosystem and the cups represent stability domains. Ecosystem can maintain their original state against multiple and stochastic disturbances through resilience-based management (indicated by the two headed arrow (a) widening the cup width). When resilience-based management fails and ecosystem degradation proceeds, forewarning indicators of ecological thresholds can be used as a backstop for avoiding negative state transitions (indicated by the arrows (b)).

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For the comprehensive management of ecosystems in the face of multiple and stochastic disturbances, it is critical to optimize the levels of key biodiversity elements by appropriate management of anthropogenic disturbances (Fig. 3). Key biodiversity elements that reinforce ecosystem resilience, and that ensure important system feedbacks and biological interactions need first to be identified. Then, how a particular biodiversity element changes in response to the anthropogenic disturbance prevailing in a given ecosystem should be determined. Although there would be a range of response patterns of key biodiversity elements to anthropogenic disturbances depending on ecosystems, disturbance types, and environmental contexts, we can expect several typical responses (Fig. 3a). If a key biodiversity element exhibits a negative monotonic response to a disturbance (Fig. 3a1), an intense anthropogenic disturbance would remarkably reduce ecosystem resilience. For example, the meta-analysis of 18 datasets from different areas of the world (Laliberté et al., 2010) demonstrated general reductions in functional redundancy and response diversity under land-use intensification such as biomass removal and fertilization. If a response pattern of key element follows a unimodal curve (Fig. 3a-2) in a similar manner to intermediate disturbance hypothesis (Connell, 1978), management that maximizes the key biodiversity element would be efficient to ensure resilience. In this case, significant reduction in the key element caused by either overuse or underuse of ecosystem resources by the cessation of disturbance (e.g., prohibiting livestock grazing, or abandoning an ecosystem use) can remarkably degrade resilience. Further, in some cases, a key biodiversity element may exhibit a neutral or even positive monotonic response to a disturbance (Fig. 3a-3,4). Despite the general reduction in functional redundancy and response diversity across different ecosystems caused by land-use intensification, Laliberté et al. (2010) also reported the neutral or positive response patterns of response diversity and functional redundancy in some forest ecosystems. Laliberté et al. (2010) suggested that this may result from a modification of environmental filters by tree harvesting that enables the establishment of herbaceous species with wider distributions of functional traits than observed in the closed forest. The increase in response diversity and functional redundancy in this case might not necessarily lead to enhance ecosystem resilience to maintain tree productivity. For the management of ecosystem resilience to maintain target ecosystem functions and services in a given ecosystem, it is therefore critical to explore underlying factors that determine response patterns of key biodiversity elements to a disturbance as well as the response patterns themselves. Finally, where there is a possibility of state transitions in an ecosystem, developing forewarning indicators of ecological threshold is fundamental so as to provide a backstop for resilience-based management (Fig. 3b).

6. Spatial and temporal perspectives In this review, we did not specify a focal temporal or spatial scale. However, to deal with the consequences of anthropogenic activities such as land-use intensification and habitat conversion, such activities should be examined from various spatial and temporal perspectives (Pasari et al., 2013). At reported levels of biotic homogenization (the process by which the genetic, taxonomic or functional similarities of regional biotas increase over time; Olden and Rooney, 2006), landscape-scale land-use intensification has serious consequences not only at larger spatial scales but also at local scales. Likewise, long-term perspectives are also essential. Recently, Reich et al. (2012) showed that the roles of each species in sustaining focal ecosystem functioning increases with time. This gradual disappearance of functional redundancy

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means that, for ensuring ecosystem resilience over a long term, probably we need a higher level of biological diversity than the estimated level by using snapshot or short-term data. Nonetheless, as long-term data are often not available, snapshot or short-term data will continue to be the primary information sources used for the application of resilience-based management and ecological threshold identification. Management prescriptions should be applied carefully in light of uncertainties resulting from the lack of long-term evidence. It is therefore important to develop tests for the validity of selected management prescriptions at appropriately large scales within the concept of adaptive management (Allen et al., 2011). Active adaptive management can be a flexible approach to complex ecosystem dynamics, but they emphasizes a learning process where alternative management prescriptions are frequently assessed based on the philosophy that knowledge is incomplete (Suding and Hobbs, 2009; Allen et al., 2011). 7. Implications and concluding remarks Ecological managers need to pay more attention to the possibility of state transitions in a target ecological system, because such changes are often irreversible, thus preventing restoration of the original system. Limited data and resources are available about ecological restoration in systems governed by complex feedbacks and interactions for system reconfiguration (Schmitz, 2004). Therefore, regardless of the reversibility or irreversibility of state transitions, management based on ecosystem resilience and ecological thresholds should remain as the center of ecosystem management. Much evidence currently supports the primary role of biological diversity in sustaining the fundamental functionality of ecosystems, and thus in maintaining ecosystem services (see syntheses by Cardinale et al., 2012; Naeem et al., 2012), and this knowledge can be applied to the management of local ecosystems. Because different species play complementary roles in stabilizing ecosystem functionality in the face of environmental fluctuations (reviewed by Gonzalez and Loreau, 2009; Mori et al., 2013), conservation of a sufficient level of diversity should have high priority. The metrics used to evaluate biological diversity should be context-dependent, and management should not rely exclusively on a single measure of biodiversity, because many studies have shown that different aspects of biodiversity, including taxonomic, functional, phylogenetic, and genetic diversity as well as community structure and composition, are important for sustaining optimal levels of ecosystem functionality (e.g., Cadotte et al., 2008; Flynn et al., 2011; Tomimatsu et al., 2013). Currently, we have limited understanding as to which elements of biological diversity confer ecosystem resilience for each of environmental and ecological contexts. In addition, the understanding of threshold dynamics and its application to ecosystem management has been in development. Nonetheless, lessons learned from previous experience with policy implementation should provide scientists and managers of ecosystems prone to multiple, and often unpredictable, disturbances with essential information. Such knowledge can take us beyond simply invoking the precautionary principle of conserving biodiversity to a predictive science that informs practical solutions to cope with uncertainties and ecological surprises (Naeem et al., 2012). Acknowledgements This review grew out of a workshop held at the 58th annual meeting of the Ecological Society of Japan, which took place from 8 to 12 March 2011 at the Sapporo Convention Center, Sapporo,

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Japan. This work was supported financially by Tohoku University’s Global COE program (no. J03) and by Yokohama National University’s Global COE program (no. E03), with additional support from a Grant-in-Aid for Young Scientists A to TS (Number 25712036) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

Appendix. List of references analyzed in the systematic review on ecological threshold. The ISI Web of Science search was conducted on March 26, 2013, for articles published after 1979 for the search term “ecological threshold(s)” in the title, abstract, or keywords. Of the 246 articles found (the one published in 1994 being the earliest), we analyzed 147 that used or mentioned the use of any quantitative method to identify an ecological threshold. The references are listed by ecosystem type ordered by the number of articles (in parentheses).

Forest/Woodland (42) Andersson, E., Bodin, O., 2009. Practical tool for landscape planning? An empirical investigation of network based models of habitat fragmentation. Ecography 32, 123–132. Arroyo-Rodriguez, V., Pineda, E., Escobar, F., Benitez-Malvido, J., 2009. Value of Small Patches in the Conservation of Plant-Species Diversity in Highly Fragmented Rainforest. Conserv. Biol. 23, 729–739. Arroyo-Rodriguez, V., Toledo-Aceves, T., 2009. Impact of landscape spatial pattern on liana communities in tropical rainforests at Los Tuxtlas, Mexico. Appl. Veg. Sci. 12, 340–349. Bassler, C., Muller, J., Dziock, F., 2010. Detection of Climate-Sensitive Zones and Identification of Climate Change Indicators: A Case Study from the Bavarian Forest National Park. Folia Geobot. 45, 163–182. Becker, D.A., Wood, P.B., Keyser, P.D., Wigley, T.B., Dellinger, R., Weakland, C.A., 2011. Threshold responses of songbirds to long-term timber management on an active industrial forest. For. Ecol. Manag. 262, 449–460. Betts, M.G., Forbes, G.J., Diamond, A.W., 2007. Thresholds in songbird occurrence in relation to landscape structure. Conserv. Biol. 21, 1046–1058. Betts, M.G., Hagar, J.C., Rivers, J.W., Alexander, J.D., McGarigal, K., McComb, B.C., 2010. Thresholds in forest bird occurrence as a function of the amount of early-seral broadleaf forest at landscape scales. Ecol. Appl. 20, 2116–2130. Bodin, O., Tengo, M., Norman, A., Lundberg, J., Elmqvist, T., 2006. The value of small size: Loss of forest patches and ecological thresholds in southern Madagascar. Ecol. Appl. 16, 440–451. Cowling, S.A., Shin, Y., 2006. Simulated ecosystem threshold responses to co-varying temperature, precipitation and atmospheric CO2 within a region of Amazonia. Glob. Ecol. Biogeogr. 15, 553–566. Cui, B.S., Yang, Q.C., Zhang, K.J., Zhao, X.S., You, Z.Y., 2010. Responses of saltcedar (Tamarix chinensis) to water table depth and soil salinity in the Yellow River Delta, China. Plant Ecol. 209, 279–290. Cumming, G.S., Southworth, J., Rondon, X.J., Marsik, M., 2012. Spatial complexity in fragmenting Amazonian rainforests: Do feedbacks from edge effects push forests towards an ecological threshold? Ecol. Complex. 11, 67–74. Digiovinazzo, P., Ficetola, G.F., Bottoni, L., Andreis, C., Padoa-Schioppa, E., 2010. Ecological thresholds in herb communities for the management of suburban fragmented forests. For. Ecol. Manag. 259, 343–349. Donner, D.M., Ribic, C.A., Probst, J.R., 2009. Male Kirtland’s Warblers’ patch-level response to landscape structure during periods of varying population size and habitat amounts. For. Ecol. Manag. 258, 1093–1101. Edwards, A.C., Russell-Smith, J., 2009. Ecological thresholds and the status of firesensitive vegetation in western Arnhem Land, northern Australia: implications for management. International J. Wildland Fire 18, 127–146. Eiswerth, M.E., Haney, J.C., 2001. Maximizing conserved biodiversity: why ecosystem indicators and thresholds matter. Ecol. Econ. 38, 259–274. Elliott, G.P., Kipfmueller, K.F., 2011. Multiscale Influences of Climate on Upper Treeline Dynamics in the Southern Rocky Mountains, USA: Evidence of Intraregional Variability and Bioclimatic Thresholds in Response to Twentieth-Century Warming. Ann. Assoc. Am. Geogr. 101, 1181–1203. Ellis, T.M., Betts, M.G., 2011. Bird abundance and diversity across a hardwood gradient within early seral plantation forest. For. Ecol. Manag. 261, 1372–1381. Fuhlendorf, S.D., Smeins, F.E., Grant, W.E., 1996. Simulation of a fire-sensitive ecological threshold: A case study of Ashe juniper on the Edwards plateau of Texas, USA. Ecol. Model. 90, 245–255. Furukawa, T., Fujiwara, K., Kiboi, S.K., Mutiso, P.B.C., 2011. Threshold change in forest understory vegetation as a result of selective fuelwood extraction in Nairobi, Kenya. For. Ecol. Manag. 262, 962–969.

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