The behavioral or evolutionary response of species or systems to a particular stress. In ecology, adaptation has a single‐species focus. Adaptation is perhaps best understood in a Darwinian sense, whereby species constantly evolve to adjust to changing environmental conditions, developing a set of traits that confer the ability to reproduce, grow and survive in habitats. With global warming, there is an increasing mismatch between such traits (e.g., thermal tolerances of cold‐stenothermic species) and changing temperature regimes, leading to maladaptation and potential extinction.
A measure of the capability of a system to adapt and change behaviourally or evolutionarily to unspecified future stresses. The concept extends the single‐species adaptation focus, emphasizing a system‐level property that focuses on learning and the constant adjustment of ecosystem properties, including community composition, function and processes to anticipate and respond to natural and anthropogenic disturbances, maintaining a system within a specific regime. It is frequently confused with adaptation or adaptive response to specific stresses, for example climate change. Adaptive capacity has been used qualitatively in the social and ecological sciences and thus varies between different contexts and systems (Adger et al. 2007). Its current use makes it either indistinguishable from, or a subset of, resilience, highlighting the need for operationalization and quantitative approaches.
Adaptive governance is an evolving research framework for analyzing the social, institutional, economic and ecological foundations of multilevel governance modes that are necessary for building resilience to cope with the vast challenges posed by global change and its effects on coupled complex adaptive systems of people and nature. Adaptive governance enables adaptive management. The incorporation of adaptive management, adaptive co‐management, ecosystem management and various forms of formal and informal integrated resource management offers promising approaches in the context of adaptive governance (Green et al. 2015).
Process of conducting management as experiments designed to enhance learning and reduce uncertainty (Allen et al. 2011). Adaptive management, often misinterpreted as a trial‐error approach, tests and recalibrates management hypotheses iteratively. It is appropriate where both uncertainty and controllability in the system are high, that is, where there are unanswered questions regarding system dynamics and response to interventions, but where intervention is also possible. Adaptive management was developed in tandem with resilience theory, as a method to probe systems without inadvertently causing a state change.
A potential alternative configuration in terms of abundance and composition, function and process, of a system. Alternative states are explicit in ecological resilience. A stable state is defined by stable structures, functions, processes and feedbacks, separated by thresholds (Lewontin 1969). Patterns, processes and feedbacks differ between alternative states. These attributes characterize the basin of attraction. The terms state and regime are often used interchangeably. However, regime specifically refers to the processes and feedbacks that confer dynamic structure to a given state of a system. A change in the process regime of a system necessarily results in a state change.
Cross‐scale resilience posits that resilience in ecological systems is enhanced when functional diversity and traits are diverse within scales and reinforced across scales. This model explicitly considers the compartmentalization of ecological patterns and processes by spatial and temporal scales. The cross‐scale resilience model was established for operationalizing and quantifying ecological resilience (Peterson, Allen & Holling 1998), and especially for understanding the relationship between biodiversity, scale and resilience. Cross‐scale resilience can be assessed by evaluating the distribution of functional traits of species within and across scales. Several approaches, including discontinuity analysis and time‐series or spatial modeling tools, are available for assessing cross‐scale resilience (Angeler et al. 2014).
Discontinuity theory recognizes the hierarchical organization of ecosystems, which is manifested in nonlinear patterns of structure, function and processes. Discontinuity theory and its application to objectively identify spatiotemporal scaling patterns in ecosystems have been pioneered using body size distribution of animals (Angeler et al. 2016). Animals of similar sizes perceive resources in the environment similarly and therefore presumably operate at a specific scale. Different aggregations of species, based on their body masses, use resources at different scales, and the resulting patterns of species body size distributions can therefore be used to delineate distinct scales in ecosystems (Nash et al. 2014). This also applies to other complex systems such as urban systems and economies (Sundstrom et al. 2014).
Ecological resilience is a measure of the amount of change needed to change an ecosystem from one set of processes and structures to a different set of processes and structures (Holling 1973). Resilience is an emergent property of ecosystems and other complex systems and recognizes that systems operate in multiple basins of attraction. From a human perspective, it is crucial because it implies a predictable, although variable, delivery of expected ecosystem services. Understanding the dynamics of resilience is critical to achieving sustainable human interactions with their supporting ecosystems. An ecosystem with high ecological resilience requires a substantial amount of energy to transition to an alternative state, whereas a low resilience system would transition with a relatively small amount of energy. Ecological resilience as a systemic phenomenon can be measured through an assessment of mutually non‐exclusive attributes, including scales, alternative states, feedbacks and thresholds.
Stability is a complex and multifaceted concept, including components such as variability, resistance, resilience, persistence and robustness (Donohue et al. 2013). Despite this complexity, concepts related to stability are reductionist in the sense that they are less integrative of the dynamic and complex system components from which resilience emanates. Simply, stability refers to the ability of a system to remain unchanged in the face of perturbation, and to return to the initial state quickly when perturbation alters system parameters. That is, stability concepts generally ignore that ecological patterns and processes are compartmentalized by distinct spatial and temporal scales or operate in different attractor domains. From a management perspective, stability is often desirable while resilience, on the other hand, considers variability to be a desirable system property, and one that through adaption and evolution infers greater ecological resilience.
Engineering resilience focuses on the return of structural and functional attributes of systems to pre-disturbance conditions following a disturbance. Rapid return times are interpreted as reflecting high engineering resilience (Pimm 1991). The unit of measurement is the time of recovery. This definition assumes that systems are characterized by a single equilibrium and therefore fails to account for the potential for alternative states of the same system. In a management context, engineering resilience incorrectly assumes that an ecosystem always recovers from a degraded state to a previous or more desirable state, and therefore, the only measure of interest is return time.
In ecological systems, feedbacks arise from the set of interactions between processes, abiotic structure and species. Feedbacks control an effect by influencing and being influenced by the process which gave rise to it. A positive feedback enhances or amplifies these processes, while negative feedbacks have the opposite effects. Positive and negative feedbacks generally do not imply any judgement of value regarding the desirability of the effects or outcomes. Positive feedbacks are of most interest in resilience theory; these help maintain ecological structure, function and processes in specific alternative states or regimes.
Functional redundancy refers to the existence of more than one species or process delivering the same ecological function, or trait. Functional redundancy is often studied without explicitly accounting for scaling relationships in ecological systems (Truchy et al. 2015). In terms of cross‐scale resilience, redundancy is considered in two forms: (i) Redundancy existing within a scale, whereby identical functions would be redundant in the strict sense. (ii) Redundancy that occurs in functionally similar species that exploit their environment at different scales. In this case, the more appropriate term is cross‐scale reinforcement because it accounts for the fact that species and processes operating at other scales can maintain a function impacted by a disturbance at a single scale.
A general and generic property of systems, the broad ability of a system to cope with disturbances without changing state. It does not define the part of the system that might cross a threshold and the kinds of shocks the system needs to deal with, and it copes with uncertainty in all ways (Folke et al. 2010). Managing for general resilience is complex given that multiple patterns and processes operating at distinct spatiotemporal scales need to be accounted for. However, this complexity may create opportunities for re‐evaluating present and learning from past situations, boosting novelty and innovation and triggering social and policy change. That is, understanding general resilience may create new possibilities for adaptive or transformative change to swiftly changing social-ecological baselines.
Refers to situations where ‘the path out is not the same as the path in’, or to the difficulty to reverse a regime change. Hysteresis operates when the observed equilibrium of a system cannot be predicted solely based on environmental variables, but also requires knowledge of the system's past history. For instance, when a system has undergone a regime change, for example a lake, if subjected to excessive nutrient loading, will unlikely return to a previous state through mitigation of nutrients alone. Feedbacks in the alternative state are stabilizing the system, and to break hysteresis, a series of interventions (nutrient elimination, food web manipulations) are required.
A shift in regime is a persistent change in the structure, function and mutually reinforced processes or feedbacks of an ecosystem (Scheffer et al. 2001). The change of regimes, or the shift, usually occurs when a smooth change in an internal process (feedback) or a single disturbance (external shocks) triggers a completely different system behavior. Shifts can be abrupt (years, decades) or transitory (centuries, millennia), depending on the time‐scale of observation (Spanbauer et al. 2014), and affect ecosystems locally, regionally or globally (Hughes et al. 2013). Regime shifts have gained importance in ecology because they can substantially affect the flow of ecosystem service provisioning to human societies.
This term is analogous to engineering resilience or ‘bounce back’ and refers explicitly to the capacity of a system to return to its initial state following disturbance. Although this definition does not explicitly exclude the existence of thresholds (e.g. the physical property of a material to return to its original shape or position without exceeding its elastic limit), multiple equilibria are not considered in this definition. More clear terms for this behavior include return time, recovery and engineering resilience.
This term has been used within ecological stability research to characterize the property of communities or populations to remain unchanged when subject to disturbance. The inverse of resistance is sensitivity (Grimm & Wissel 1997). As is the case with most ecological stability definitions, relevant patterns and processes are considered from a single equilibrium point of view and do not account for the existence of thresholds. Walker et al. (2004) broadened the use of this concept to consider resistance to be a component of ecological resilience.
Rather than focusing on the redundancy of a specific functional trait across scales, this concept emphasizes the variation in responses to environmental change by species within a functional group within scales (Elmqvist et al. 2003). Response diversity considers the functional make‐up of a species accounting for multiple traits that modulate species responses to disturbances through, for instance, distinct colonization, growth, competition and dispersal abilities. Variability in combinations of multiple traits increases the adaptive capacity to cope with and respond to disturbances, maintaining functional patterns and processes in ecosystems.
Scaling has received different interpretations but in ecology refers to the spatial extent and temporal frequency, of a specific set of processes or structure. These processes differ across different scales but are commensurate in space and time (i.e. fast processes operate at small spatial scales, while slow processes occur over broad spatial extents). That is, ecological scaling has a dual nature, meaning that it encompasses both the spatial and temporal dimensions of processes and structures (Angeler, Göthe & Johnson 2013). Ecological studies are often carried out in a temporal or spatial context without both ‘scales’ necessarily matching each other. In these circumstances, the scope of study, whether ‘temporal’ or ‘spatial’ scaling, needs to be made explicit.
A variable whose rate of change is slow with respect to the timescale of ecosystem provisioning and management. For practical purposes, these variables are considered constant. However, because these scales can exist beyond our lifetimes, they are difficult to detect and predict.
The resilience of a system or component of a system to a known, or anticipated disturbance. The resilience of what, to what (Carpenter et al. 2001). A management focus on specified resilience can become problematic because increasing resilience of particular parts of a system to specific disturbances may cause the system to lose resilience in other ways. For instance, international travel in Europe became increasingly focused on developing air travel, while international ground and water transportation were deemphasized. The volcano eruption in Iceland in 2010 uncovered the low resilience of this transportation system to the extensive cloud of ash in the air that interfered with the operation of aircraft.
Thresholds indicate that ecosystems can undergo a shift between alternative states when critical disturbance levels are surpassed (Suding & Hobbs 2009). Thresholds are equivalent to tipping points and may be detected as discontinuities or bifurcation points in complex systems. Thresholds emphasize that an ecosystem and other complex systems undergo a fundamental reorganization in structure, functions, processes and feedbacks, and need to be distinguished from statistically detectable curvilinear patterns within ecosystem states.