Catastrophic regime shifts in ecosystems: linking theory to observation

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Review TRENDS in Ecology and Evolution Vol.18 No.12 December 2003 653(a) (b) (c)201.050160.540120.030208–0.5–1.0100401965 1970 1975 1980 1985 1990 0 20 40 60 80 100Ecosystem state indexNumber of casesYearFloating plant cover (%)Shade (E Z)0.0 0.1 0.2Total-P (mg l –1 )0.3TRENDS in Ecology & EvolutionFigure 3. Three types of hints of the existence of alternative attractors from field data: (a) shift in a time series, (b) multimodal distribution of states, and (c) dual relationshipto a control factor. The specific examples are (a) regime shift in the Pacific Ocean ecosystem (shaded) (modified with permission from [4]), (b) bimodal frequency distributionof free floating plants in a set of 158 Dutch ditches (modified with permission from [6]), and (c) different relationships between underwater shade and the total phosphorusconcentration for shallow lakes dominated by Cyanobacteria (blue circles) and lakes dominated by other algae (open circles) (modified with permission from [62]).various alternative explanations for convergence to differentendpoints. However, path dependency can be exploredexperimentally. For instance, different orders of colonisationfrom a common species pool have been shown toresult in alternative endpoint communities that are allstable in the sense that they are resistant againstcolonisation by other species from the pool [53].Disturbance can trigger a shift to another permanentstate (Figure 4b)The presence of alternative attractors also implies that asingle stochastic event might push the system to anotherbasin of attraction from where it converges to an alternativepersistent regime. Field manipulations can showthis rather convincingly. For instance, a temporary reductionin the fish stock (biomanipulation) of one of the turbidEnglish gravel pits mentioned above has induced a shiftto a long-term clear state [52]. Lasting effects of singledisturbances have also been studied in ecotoxicologicalresearch, where the inability of the system to recover to theoriginal state after a brief toxic shock has been referred toas ‘community conditioning’ [54]. Such experiments shouldbe interpreted cautiously. The return of the original speciesshould not be prevented by isolation of the community.Another problem is the potentially long return time toequilibrium, which can suggest an alternative stableregime even if it is just a transitional phase. For instance,the biomanipulated Lake Zwemlust (the Netherlands)remained clear and vegetated for six years until it startedslipping back to the turbid state [55].Hysteresis in response to forward and backward changesin conditions (Figure 4c)Demonstration of a full hysteresis in response to a slowincrease and subsequent decrease in a control factor alsocomes close to proving the existence of alternative attractors.Examples of hysteresis are seen in lakes recoveringfrom acidification [56] or eutrophication (M.L. Meijer,PhD Thesis, Wageningen University, 2000; [10]), andin hemlock-hardwoods forests responding to change in(a) (b) (c)6100Floating plant biomass (g)54321% of lake area coveredby vegetation8060402000 00 1 2 3 Control 1 2 3 40.00 0.05 0.10 0.15 0.20 0.25 0.30Submerged plant biomass (g) YearTotal-P concentration (mg l –1 )% of lake area coveredby charophytes40302010TRENDS in Ecology & EvolutionFigure 4. Three types of experimental evidence for alternative attractors: (a) different initial states leading to different final states, (b) disturbance triggering a shift to anotherpermanent state, and (c) hysteresis in response to forward and backward change in conditions. The specific examples are (a) path dependency in growth trajectories fromcompetition experiments of a submerged plant (Elodea) and a floating plant (Lemna). Different lines represent the development of the two species in the experiments rununder identical conditions, but starting from, different initial plant densities (reproduced with permission from [6]); (b) shifts of shallow lakes to a vegetation-dominatedstate triggered by temporary reduction of the fish stock. Each line represents a different experimental lake (modified with permission from [63]); (c) hysteresis in theresponse of charophyte vegetation in the shallow Lake Veluwe (the Netherlands) to an increase and subsequent decrease in the phosphorus concentration (modified, withpermission, from M.L. Meijer, PhD Thesis, Wageningen University, 2000).http://tree.trends.com
654Review TRENDS in Ecology and Evolution Vol.18 No.12 December 2003disturbance intensity [28]. However, a hysteretic patternmight not indicate alternative attractors if the response ofthe system is not fast enough relative to the rate of changein the control factor. Indeed, one will always see somehysteresis-like pattern unless the system response is muchfaster than the change in the control variable.In conclusion, experiments can be a powerful way toshow that a system has alternative attractors. There areobvious limitations to exploring large spatial scales andlong time spans. However, small and fast systems, whichare more tractable experimentally, can help us to inferwhat might happen in larger and slower ones.The contribution of modelsIn addition to field data and experiments, models remaina central ingredient of most of the work on alternativeattractors. Discussion of the long history of confusion overthe question of what models can tell us exactly is beyondthe scope of this review, but we highlight some crucialpoints of interest.Qualitative reasoningAlternative attractors require positive feedbacks, andchange among attractors implies a shift in dominantcontrolling feedbacks. For instance, in stratified lakes, aclear-water state prevents anoxic phosphorus recyclingfrom the sediments, which, in turn, limits phytoplanktongrowth. By contrast, in a turbid state, high algal biomassboosts anoxic phosphate recycling, promoting more algalgrowth [10]. Positive feedbacks are sometimes interpretedas being almost synonymous with the possibility of switchesbetween alternative attractors [57]. Although this mightseem reasonable intuitively, positive feedbacks only lead toalternative attractors if they are sufficiently strong. Thus,qualitative reasoning is not sufficient to infer alternativeattractors from observed feedback loops.Simple mathematical modelsMathematical models provide a much more powerfultool than qualitative reasoning for showing that certainmechanisms can lead to phenomena of interest such asalternative attractors. Most models in the literature areso-called minimal models that focus on a minimal set ofmechanisms needed to produce a certain behaviour [58].Such models have been useful for exploring mechanismsthat are too intricate to grasp from common sense alone,and also serve well for exploring the fundamental implicationsof important aspects such as spatial heterogeneity[30,31,33] and environmental fluctuations [16,17,22].Large simulation modelsA drawback of simple models is that they necessarily leaveout many potentially important aspects. This has invokedthe development of much criticised [58] big simulationmodels intended to provide quantitative predictions.Although true validation of such predictive models issimply impossible [59], they remain very important. Onesimply cannot do appropriate experiments with largeimportant systems, such as oceans and the atmosphere.In these situations, our best hope is a combination ofmulti-interpretable records of past behaviour with‘plausible models’ of the system.In conclusion, different models have different strengthsand weaknesses, and the best strategy is obviously to usedifferent models in parallel. Each model is a ‘lie’ in thesense that it is an imperfect representation of reality.However, if various independent models coincide in predictingalternative attractors, then one can adopt thephilosophy that ‘the truth is the intersection of independentlies’ [60].ProspectsClearly, we have advanced much over the past decade inbridging the gap between abstract theory and ecosystemdynamics observed in the field. Field data are exploredwith more powerful techniques, and models are beingused to address implications of important aspects suchas environmental fluctuations and spatial complexity.Because neither field patterns nor models alone can beconclusive, it is also crucial that well-designed experimentsare being used now to demonstrate alternativeattractors. Still, although strong cases have been made, itremains remarkably difficult to prove the existence ofalternative attractors in the field. This is perhaps notsurprising in view of the historical difficulties in demonstratingthe general importance of virtually all importantmechanisms (e.g. competition, chaos or density dependence)in ecology. However, in view of the formidableconsequences of alternative attractors for conservationBox 4. Outstanding challengesObtaining better clues from field dataThere is a wealth of ecological time-series and cross-systems datathat remain to be explored. Finding hints of alternative attractorsrequires the use of novel techniques [18]. Perhaps even morechallenging is the question of how we can obtain early warningsignals of regime shifts from time-series data [18]. Foreseeing shiftsis probably very difficult [1], but it has been suggested that subtlechanges in the fluctuation pattern such as ‘reddening’ can serve as aclue of an upcoming collapse [70].Building more realism into simple modelsTheory has so far been restricted largely to the easiest case of afew species interacting in a spatially homogeneous (‘well mixed’)and constant environment. It seems important to pursue crucialaspects that have been largely neglected such as spatial heterogeneity[30,31,33], seasonality [31,36] and environmental fluctuations[16,17,21,22]. Also, empirical relationships can be linked into simplemodels to give transparent quantitative predictions.Performing crucial experimentsControlled experiments are obviously the most powerful way todemonstrate alternative attractors and to reveal underlying mechanisms.A real challenge here lies in experiments on realistic spatialand temporal scales [71]. Small-scale experiments can be veryhelpful in explaining large-scale patterns [6,47], but this has beenpoorly explored.Designing smart restoration strategiesInsight into alternative attractors can be used to design unconventionalrestoration methods [72,73]. In particular, the idea of takingadvantage of climatic extremes such as El Niño events to induce ashift to a desired regime [27] is an exciting, novel development.http://tree.trends.com
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