Resilience in aquatic ecosystems - hysteresis, homeostasis, and ...

Resilience in aquatic ecosystems—hysteresis, homeostasis, 

and health 

C. S. Reynolds 

Algal Modelling Unit, Centre for Ecology and Hydrology, GB-LA22 0LP AMBLESIDE, Cumbria, UK 

The resilience of a system refers to its ready ability to recover structure and behavior in the face of external 

forcing. The term has been used in limnetic ecology for two separate phenomena. In one, it refers to the hysteretic 

persistence of supportive capacity in the face of a managed reduction in the resource supply, giving perplexing 

delays in the recovery of lakes subject to treatment to reverse the symptoms of eutrophication. In the 

other usage, resilience is the homeostatic damping of the effects of chaotic environmental variability on community 

structure, certainly within quantifiable thresholds, in all aquatic ecosystems. A simple energetic model 

of ecosystem function is developed in order to assimilate the two types of resilience. Although provisional terms 

are coined to emphasize their distinction, only structural resilience describes a general property of systems and, 

which, moreover, assists an understanding of their health and ascendancy. Resourcing resilience owes to the 

separation of the loading and the growth responses in particular kinds of water body but otherwise reveals little 

that is not already well known. No strong case can be made for persisting with the latter usage and certainly 

not without the qualification. 

Keywords: lakes, trophic links, eutrophication, energy balance, system, trophic, ecosystem 

Introduction 

When I was invited to prepare an opening presentation 

to introduce a workshop on “The Resilience and 

Integrity of Aquatic Ecosystems” it was clear that I 

would have to provide a considered definition of 

resilience and a careful development of the principles 

and mechanisms underpinning its essential concepts. 

This requirement was emphasized because some of 

preceding discussions had been directed towards the 

measures applied to lakes to reverse the symptoms of 

eutrophication and to restore water quality, and to the 

sometimes lengthy periods required for the therapies 

to secure the desired improvements. However, this 

behavior seems to have little to do with the more usual 

ecological application of resilience, referring to the 

ability of an ecosystem to absorb environmental variation 

and to recover its steady state after disturbance. 

Thus, a first objective of this introductory essay is to 

distinguish between the two usages, to find the conceptual 

common ground between them, and to explore 

how the concept might be turned into a quantifiable 

framework for securing the integrity and functional 

health of individual systems. 

Let us begin with the word itself. Resilience is the 

ability of an entity to recover its original form after 

compression or distortion by an applied force. This 

idea transfers quite readily to the behavior of ecosystems 

subject to a variety of external physical forces, 

each of which may impair temporarily structure and 

function within the impacted systems, yet these may 

be rapidly regained, once the forcing is relaxed. The 

use of the term system resilience, in the context of 

lake eutrophication and, in particular, the persistence 

of symptoms of enrichment—among them large algal 

crops and high turbidity—long after the enriching 

Aquatic Ecosystem Health & Management, 5(1):3–17, 2002 © 2002 AEHMS 1463-4988/02 $12.00 + .00 

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Reynolds/ Aquatic Ecosystem Health and Management 5 (2002) 3–17 

external sources are truncated, owes principally to 

Sas (1989). In this instance, the restorative therapy, 

the reduction in external nutrient loading, is the forcing 

agent but the anticipated response of a reduced 

algal biomass response is neither immediate nor even 

linear. Several mechanisms are known to contribute to 

this behavior, including the buffer provided by unused 

supportive capacity and the recyclability of nutrient 

resources within the system (‘internal loading’). This 

buffering capacity is not inexhaustible but, in terms of 

management and of returns on investment, it can seem 

stubbornly persistent (Søndergaard et al., 1993). 

Supposing the capacity of the impacted system to 

have once supported lower algal and plant biomasses, 

the eutrophication and the protracted recovery are 

clearly parts of a strongly hysteretic behavior pattern. 

During the delay period, at least, the resilient system 

fails to regain a state of improved health. 

In almost every other ecological context in which 

the word has been used, resilience refers to the collective 

capacity for homeostasis that is acquired by 

developing ecosystems. Before the advent of a subdiscipline 

dealing with the large-scale properties of 

systems (‘macroecology’: Brown and Maurer, 1989), 

the manifestation of a functionally mature ecosystem 

was considered to be its stability. The notion of a sort 

of self-maintaining equilibrium condition, with very 

little fluctuation, was memorably challenged by 

Holling (1973). He recognized the scales and amplitude 

of normal, natural environmental variability that 

surviving systems have successfully to absorb. Their 

ability to do so is a measure of their resilience. Thus, 

resilience is the property of the system that keeps it at 

some recognizable steady state and permits it to return 

there following disturbances wrought by external 

forcing. It conforms to the role of a Lorenzian attractor 

of Chaos Theory in suppressing random behavior 

(Gleick, 1988). Thus, the supposed stability of a system 

is the manifestation either of its accommodation 

of mild environmental fluctuations or of the speed 

with which it is able to return to an erstwhile steady 

state. Provided the system retains the inocula of component 

species from which new populations may 

develop, it may well recover its former structure 

(Harrison, 1979). In other words, and in contrast with 

the earlier definition, the resilient system is one that is 

able to regain quickly a state of perceived health. 

It would be relatively simple to declare that these 

contrasted and seemingly opposed usages of 

resilience are unnecessarily confusing and that one of 

them should be rejected. Neither would it be too 

difficult to suggest that, because of the narrowness of 

its application and because the property it described 

is, in any case, closer to the understanding of resistance 

than to resilience, Sas’ (1989) usage should be 

the one that is discarded. Before that happens, however, 

the opportunity should be taken to explore the conceptual 

commonality of both usages to the adverse 

anthropogenic impacts on the health of aquatic 

ecosystems. More to the point, we need to assure ourselves 

of the mechanisms of ecosystem resilience, the 

factors that contribute to and achieve the so-called 

steady states and the critical levels of forcing that 

might move the system to some other steady state. 

Here, I seek to formulate a model that incorporates 

the assembly of aquatic systems, their robustness and 

the levels of forcing they may tolerate: the objective is 

a single view of ecosystem function that subsumes 

both understandings of system resilience. 

The structure of aquatic ecosystems 

Functional ecosystems comprise complex consortia 

of organisms with closely interrelated activities 

that together process the resources of geographicallydistinct 

segments of the planet into living biomass. 

Most of the driving energy for this processing comes 

from sunlight which has to be captured, stored chemically, 

and then released in a regulated manner to sustain 

the work involved in gathering and assembling 

resources into biological structures. At their most fundamental 

level, these exchanges are biochemical and 

they occur at the level of cytological structures—light 

harvesting centres, ribosomes, and mitochondria—the 

assembly of which is coordinated within cells, according 

to instructions copied in their DNA, each time it is 

replicated. Cells form tissues, the specialist functions 

of which contribute to the physiological workings of 

whole organisms, whose nature is to grow and reproduce. 

Organisms are, in turn, functionally differentiated 

among energy-capturing primary producers and 

resource-cycling heterotrophs. Their populations contribute 

to the ecological makeup of communities and 

their mutual interactions, most significantly in governing 

the trophic linkages of the food web, determine 

the eventual structure of the ecosystems. 

This much is well understood but there are two 

aspects that require emphasis. One is the continuous 

nesting of the relevant processes, from molecule 

and photon up to the material and energy budgets 

of whole ecosystems. In order to comprehend and 

quantify large-scale functions, it is justifiable to 

view ecosystems as fractal series, each level being

Reynolds/Aquatic Ecosystem Health and Management 5 (2002) 3–17 5 

essentially quantifiable in common terms of resource 

processing and energy dissipation. Such series establish 

the basis for the achievable norm of a steady-state 

condition (Pielou, 1974). Second, while the ecosystem 

remains the sum of the component processes carried 

out by the aggregate of its constituent individuals, the 

highest level of directed, internal control is located in 

the genomic instructions of the individual component 

organisms. Every aspect of the behavior of structures 

involving populations of interacting species becomes 

contingent upon the most likely outcomes: the supposed 

ability of ecosystem structures to self-organize 

is a wholly emergent consequence of interactive 

responses to environmental variability (Reynolds, 

2001). 

Aquatic ecosystems are scarcely less complex than 

are terrestrial ones. However, the traditional view of 

the interrelationships among the biota of large water 

bodies provides a convenient introduction to highorder 

behavior. They are supposed to be built and 

organized around a flow of organic carbon through a 

structured food web, which, as in the above ideal, 

involves the autotrophic primary producers (the phytoplankton), 

a series of phagotrophic consumers (zooplankton, 

planktivorous, and piscivorous fish), and 

heterotrophic micro-organisms (bacterioplankton) 

closing a material cycle. Carbon dioxide is fixed and 

assimilated into phytoplankton biomass, which, in 

turn, becomes the food of the pelagic phagotrophs and 

the substrate of the heterotrophs. 

It is emphasized that this simplistic view of the 

structure is no longer literally valid. In the first place, 

terrestrial producers fix much of the organic carbon 

arriving in freshwater systems. Some is particulate 

and its presence in small ponds and streams has long 

been recognized to support macroinvertebrate shredders 

and detritus feeders. Much arrives in the form of 

fulvic and humic soil leachates, derived ultimately 

decomposing plant material. Attention has been 

drawn by several authors (Salonen et al., 1992; 

Wetzel, 1995) to the fact that this dissolved fraction 

represents some 50 to 90% of the organic carbon in 

the open water of lakes, more than is represented by 

the aggregate of live biomass. The extent to which 

aquatic ecosystems are heterotrophic (Thomas, 1997) 

is not resolved and it is far from clear that this refractory 

organic carbon is readily converted to biomass. 

Even in the nutrient-deficient, ultra-oligotrophic 

oceans, containing up to 1 mg DOC l –1 , the 

heterotroph biomass is usually found to be carbonlimited 

(Bratbak and Thingstad, 1985). On the other 

hand, there is a rapid turnover of relatively low molecular 

weight photosynthetic alternatives (such as glycolate) 

produced by phytoplankton whose prospects 

for biomass increase are constrained by severe nutrient 

limitation and whose photosynthate and oxidant 

by-products cannot be stored. Among the green 

algae, glycolate is further oxidized (photorespired) to 

carbon dioxide and water, but, in the diatoms and 

Cyanobacteria, the glycolate is released into the medium, 

at reported rates equivalent to up to 92% of the 

contemporaneous carbon uptake rate of the cells 

(Geider and Osborne, 1992). 

Certainly at the slight, dilute producer concentrations 

obtaining, the bacteria constitute a more significant 

potential resource to the consumer network, 

though they are most efficiently gathered and progressively 

concentrated by phagotrophs working at 

the same nano- and micro-scales. The widespread 

demonstration of a microbial loop (Azam et al., 

1983) of heterotrophic bacteria, nanoflagellates and 

ciliates linking photosynthesis to crustacean zooplankton 

emphasizes its pivotal importance to the 

transfer of autochthonous carbon within chronically 

nutrient-deficient systems. Although its role in oligotrophic 

systems is emphasized, microbial intervention 

is not unimportant in the structure of eutrophic 

food webs. Where a larger producer biomass is supportable, 

filter-feeding crustacea (especially daphniids) 

may feed very effectively on microplanktic algae 

(Lampert et al., 1986), at times reducing their standing 

biomass to trivial levels. Microplanktonic ciliates 

are capable of similar destruction of the main carbon 

resource (Dryden and Wright, 1987). However, in 

small and/or shallow lakes able to support a macrophytic 

vegetation, a parallel avenue of heterotrophically 

mediated carbon flow may be established. 

Mechanical disintegration of spent plant tissue, significantly 

assisted by bacterial decomposition, 

through the action of invertebrate shredders, collectors, 

and filterers, yield a food resource to fish largely 

independent of planktic producers. Detritus and 

bacteria may even sustain littoral populations of filterfeeding 

Cladocera (including daphniids) to the extent 

that they keep the water free from planktic microalgae 

(Scheffer and Jeppesen, 1998: Søndergaard and Moss, 

1998). In this attainable steady state, the macrophytes 

are the dominant primary producers. 

A further paradigm shift from the traditional foodchain 

model concerns the production of fish biomass. 

With the exception of the true pelagic, where specialized 

feeding and filtering gill-raker adaptations for

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collecting dilute calanoid zooplankton are evident 

among their (typically) clupeoid representatives 

(shads, smelts, coregonids, and salmonids), fish biomass 

and growth are often demonstrably unsustainable 

on the basis of production in the zooplankton. 

Elsewhere, littoral and benthic foraging is required to 

sustain the resource requirements of more intensive 

and more diverse production of adult fish. The underpinning 

primary products may emanate from sedimenting 

phytoplankton or be augmented, substantially 

in the case of smaller water bodies, from aquatic 

macrophytes and/or inwashed organic matter. Among 

these small lakes, feeding activities of fish populations, 

including recruiting juveniles, “cascade” 

(Carpenter et al., 1985) through the substantial cropping 

of zooplankton to mitigate the impact of grazing 

on the phytoplankton (Carpenter and Kitchell, 1993). 

There now seems little doubt that the complexity of 

interactions arising through resource exploitation and 

cropping contributes to the richness of fish species 

that may co-exist in a lake (Hairston et al., 1960; 

Vander Zanden et al., 1999). 

This present appreciation of the structure of the 

carbon pathways in freshwaters recognizes the enduring 

contribution of autotrophs (aquatic or otherwise), 

a series of phagotrophs and, indeed, of heterotrophic 

microorganisms. There remains a need to investigate 

the provenance of the particulate organic carbon fluxes 

in selected lakes, and to determine how much is 

actually autochthonous. Jónasson’s (1996) comparisons 

of the sedimentary fluxes in various lake types 

covered a range between 90 and >300 g C m –2 y –1 . On 

the other hand, it is difficult to argue for a supporting 

flux of atmospheric carbon dioxide, entering exclusively 

across the air-water interface of much more 

than 50 to 70 g C m –2 y –1 (Reynolds, 1996, 1999). 

The shortfall may be met by dissolved carbon dioxide 

supplied in inflow streams (Maberly, 1996), but its 

photosynthetic fixation would be aquatic. On the 

other hand, inflows to particular lakes have been 

shown to bring in a substantial load of particulate 

organic carbon that is either sedimented or consumed 

by the lake’s heterotrophs. Organic loads are not necessarily 

diagnostic of pollution but may contribute to 

the normal carbon dynamics of a lake. The direct consumption 

by zooplankton of catchment-derived 

organic debris in Loch Ness (Jones, 1992) may not be 

at all unusual. 

The building of aquatic ecosystems 

For the present, the concern is not the precision of 

the structure of the trophic network described, nor 

even whether the fund of organic carbon is generated 

in the water body itself or on the land. The preceding 

section identifies the central flow of organic carbon, 

from its synthesis in primary producers, through the 

food web to its final dissipation by the heterotrophs. 

The present section seeks a comparably reductionist 

view of how such organization comes about. In so 

doing, we should keep in mind a general picture of the 

above structure but we must not simply try to explain 

the assemblages we observe. Even the issue of where 

the essential control resides—at the bottom, among 

the producers, or at the top, among the consumers, or 

on the way down again, among the decomposers— 

is avoided. What is required is an understanding of the 

powering mechanisms and processing constraints and 

the nature of the outcomes. 

The strength of the ecosystem lies in its power. 

Thus, the capture and chemical fixation of energy in 

photosynthesis is the foundation of the building 

ecosystem. In order to develop the gathering potential, 

it is necessary to be able to deploy photosynthate into 

assembling further structures for photointerception 

and photoconversion. Growth and replication provide 

the means of harnessing more of the solar power 

available and siphoning its energy into the biological 

components. These include the phagotrophs and the 

decomposers that exploit the energy store into building 

their own biomass, once they have covered the 

costs of gathering it. 

Common to each of these component steps is the 

ability for growth and replication to occur, provided 

the energy and resources are available, and that it can 

be realized provided that the energetic costs of their 

accumulation and the losses to other levels are 

exceeded or, at least, balanced by the energetic 

income. This makes it sound like a simple profit and 

loss account, and that is just what it is. Indeed, it is this 

logic which underpins harvest theory for forestry and 

fishery management. The growth-reproduction curves 

developed by Ricker (1954) and the stock-recruitment 

models considered by Gulland (1983) extrapolate the 

potential biomass losses and fishery exploitation 

against the capacity of the population to recruit subsequent 

generations and to increase the standing


stock. A positive balance is essential to population 

growth and a balance may represent some kind of ecological 

stability but a negative balance represents a 

population in decline and in danger of extinction 

through over-exploitation. One of the interesting features 

of these models is that no constancy in the rates 

of growth is assumed: they are sensitive to the effects 

of stochastic food shortages (resource depletion) or 

inhospitable conditions of cold or warmth (processing 

constraints) and the manager is required to judge the 

impacts on subsequent recruitment rates and the tolerable 

limits of exploitation. If the recruitment is too 

poor or the exploitation too heavy, he will judge correctly 

that the stock will shrink. He must also attempt 

to judge whether the population is likely to recover in 

the future or whether it is permanently impaired to a 

level from which recovery is impossible or protracted. 

Such activities amount to assessments of the 

resilience of the population to variable rates of survivorship 

and recruitment success in the face of stochastic 

environmental stresses. Fluctuations evoke 

damped responses if the relevant excess capacity at 

the lower scale does not fall below the minimum 

required at the higher scale. Thus, the heavily fished 

population can maintain its power if a larger number 

of juveniles makes it to maturity. The algal population 

continues to self-replicate in reduced light so long as 

the biomass-specific rate of photosynthesis continues 

to supply sufficient fixed carbon to supply the fastest 

attainable growth rate. 

In this way, the comparison with a balance sheet 

provides a realistic analogue of how individuals, populations 

and interactive systems build biomass and 

acquire resilience. When the total income of organic 

carbon exceeds the total running expenditure (respiration, 

repair, harvesting effort), there is a profit available 

to reinvest in growth. If the total income fails to 

meet the running costs, then the structure is unsustainable 

and some regression is inevitable (individuals lose 

weight, populations experience loss of individuals, systems 

become stressed and lose structure). Fluctuations 

in income that nevertheless maintain a variable, positive 

balance may support erratic performance but a 

foundation for recovery remains intact—the structure is 

resilient to environmental variation! 

We may go a step further in the graphical representation 

of the resilience of community and ecosystem 

structure by expressing large-scale balances in 

terms of thermodynamic exchanges. Here, the energetic 

costs of maintaining the assembled biomass may 

be compared directly with its energy-harvesting 

potential. Again, the system that captures more shortwave 

energy than it dissipates as heat is able to build 

more capacity and so move the structure further from 

energetic equilibrium. 

This positive counter-current to the entropic breakdown 

of structure has been called negentropy or 

exergy (Mejer and Jørgensen, 1979; Jørgensen, 1982). 

System exergy is a convenient aid to interpreting biological 

dynamics. It represents the current trading balance 

of the system in question, some of which may be 

reinvested in increasing trade. Moreover, so long as it 

is positive, there is a slack, a sort of buffer (Nielsen, 

1992) or cushion, against income fluctuations and 

small losses. 

Nielsen’s (1992) diagrammatic representation of 

the exergy buffer provided the original inspiration for 

the drawing employed here (as Figure 1), which is 

simplified from an earlier attempt to quantify the 

exergy of an aquatic system and to determine its liability 

to be exceeded (Reynolds, 1997). It must be 

first emphasized that the system represented was 

itself reduced to absolute simplicity, initially comprising 

a population of a single photoautotrophic species 

(having the properties of a laboratory strain of the 

green alga, Chlorella, operating in an environment 

where the only variable is the energy income. 

Temperature is even at 20º C and there is no nutrient 

limitation imposed on growth or achievable biomass. 

Thus the representation is of maximal energy harvesting 

as a function of harvester biomass. The vertical 

axis stretches from zero (no income or no biomass) to 

the maximum harvestable income of solar energy, 

scaled in MJ m –2 d –1 . The nominated maximum, corresponding 

to daily flux of photosynthetically-available 

solar radiation penetrating the atmosphere to 

reach the earth’s surface is set at 12.6 MJ m –2 (which 

is about 47% of the total reaching the earth’s surface), 

and representing a mean photon flux density of ~ 8 × 

10 20 m –2 s –1 or ~ 1.33 mmol photon m –2 s –1 . Into this 

light field, we start to place healthy, grown, quasispherical 

cells of Chlorella (diameter of 4 mm, or 4 × 

10 –6 m), projecting an area of 12.6 × 10 –12 m –2 

and containing about 7 pg (~ 0.6 × 10 –12 mol) of 

structural carbon and about 0.14 pg chlorophyll a pigment. 

Then, supposing that each light harvesting centre 

(LHC) comprises some 200 to 300 molecules 

of chlorophyll, each having a molecular

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Figure 1. Comparison of the potential energy harvest of a primary-producing 

biomass with the fixed, biomass-dependent maintenance requirement. The 

excess of energy income over maintenance expenditure is the energy flux of the 

system and which, while positive, provides a margin of structural resilience to 

external variability. Modified from Reynolds (1997). 

weight of 894 Da, an approximation that 1 g 

chlorophyll a is organized into about 3 × 10 18 LHC 

receptors, each Chlorella cell is able to present 

~ 0.4 × 10 6 receptors in the path of the unmodified 

photon flux, with a spatial density of 

32 × 10 12 m –2 . 

Cells may harvest only light falling on the cell. At 

low cell concentrations, most of the light is unused; 

the amount of energy that is intercepted is, theoretically, 

in direct proportion to the intercepting biomass. 

As the number of cells is increased, there is an 

increasing probability that the projection areas of the 

cells interact (they “self-shade”) and the flux intercepted 

per unit biomass ceases to be linear. With uniform 

distribution of cells, the threshold of non-linearity 

occurs at a population of 1/(12.6 × 10 –12 ) cells 

m –2 , or ~ 0.048 mol cell C m –2 . Thereafter, further 

gain in harvesting capacity as a function of producer 

biomass is sublinear and asymptotic towards a point 

where the entire photon flux is harvested. The minimum 

concentration of LHCs to do this is determined 

by the reaction rate, for the energy of the intercepted 

photon is not captured if the LHC is effectively closed 

in the action of processing the previous photon intercepted 

(Kolber and Falkowski, 1993). At 20º C, the 

LHC saturates at about 250 reactions s –1 . Thus, the 

density of LHCs required to harvest the full photon 

flux of 8 × 10 20 m –2 s –1 is not less than 32 × 10 17 m –2 , 

requiring the activity of not fewer than 80 × 10 11 cells 

m –2 , containing 4.8 mol cell carbon m –2 . 

There is no reason why a greater but less intensively 

active photosynthetic biomass should not be 

supported to do the same work, though only to the 

limit of its maintenance costs. Taking the basal 

respiration of Chlorella to be 1.1 × 10 –6 mol C (mol 

cell C) –1 s –1 (Reynolds 1990), the daily energetic cost 

of replacing 0.095 mol C (mol cell C) –1 was calculated 

to be about 1.2 MJ (mol cell C) –1 , meaning that


the maximum sustainable biomass is about 10.4 mol 

(125 g) carbon m –2 . Some of the assumptions in that 

calculation may now be viewed as inappropriate and 

employing more realistic assembly costs of ~ 470 kJ 

per mol organic carbon, a supportable maximum biomass 

of about 25 mol carbon (300 g) m –2 may be proposed. 

Both figures accommodate comfortably 

reported natural maximal densities of phytoplankton 

(~ 50 g C m –2 ) and the active parts of terrestrial plant 

biomass (~ 75 g C m –2 : see Margalef, 1997). It is a 

good deal less than the 20 to 30 kg C m –2 standing 

crops of tropical forest stands, though this is, of 

course, dominated by the accumulated necromass that 

is wood. 

The numbers used to construct Figure 1 are less 

important to the present argument than the shape that 

they generate. In systems supporting low biomass and 

incurring low maintenance costs, there is a large 

buffer of spare harvesting capacity which is available 

to fund the provision of new harvesting biomass. It 

will also provide a cushion against exploitation by the 

consumer organisms of the phagotroph tier, without 

loss of standing crop or growth potential, though it is 

sustained at the cost of reduced investment. It is also 

a cushion against submaximal solar energy income, 

which, at this scale, is consequential upon variable 

cloud cover, day length and declination. Wind mixing 

and plankton entrainment to depth in lakes and seas 

also exacts a heavy toll in light absorption which can 

be quantified and set to subtract from the potential 

energy income in Figure 1. However, so long as the 

energy harvesting can satisfy the maintenance cost, 

the existing structure can endure and recover quickly 

from the setbacks of environmental variability. 

The model can be adapted to represent the balance 

between almost any set of anabolic assembly and 

catabolic destruction, so long as they are amenable to 

rendering in energy fluxes in comparable units. At the 

scale of the individual cell, it summarizes the ability 

to increase mass and divide; at the level of the population, 

it represents the potential to increase the crop. 

It does not yet explain the interactions of different 

populations with each other, but the supposition is 

advanced that the individuals and the species that are 

able to invest most in new or additional structure are 

those most likely to contribute to the assembly at the 

next level (strongest-growing individuals contribute 

most to the populations; strongest-growing populations 

are, at least starting from equal abundance, most 

likely to dominate communities). In other words, 

rapid structural anabolism also demands high exergy 

margins. This is a decisive mechanism about how the 

dynamics of individuals— the only organizational 

level under direct genetic control—eventually feed 

upwards to determine the development of communities. 

The emergent behavior, expressed in the assembly of 

functional ecosystems, relates to the exergy arising from 

the activities of the most efficient individuals. 

This, in turn, establishes the pattern of development 

in the building ecosystem. The apparent ability 

of communities to organize themselves (autopoesis) 

around the fluxes of usable energy has, for long, promoted 

speculation and debate about its mechanisms 

and controls, and about what Straškraba (1980) called 

“the goal-seeking behavior” of systems. The principle 

of maximum exergy permitting maximal structural 

dynamism (Jørgensen, 1982) is but one of several 

thermodynamic explanations for the high-order patterns 

of ecosystem. That of Odum (1982) argued for 

the selection of organisms and structures that make 

the best use of the available power, while Ulanowicz 

(1986) regarded system growth and development as 

the simultaneous increase in system throughput, using 

increasingly complex structural networks accumulating 

more information. These theories are not mutually 

exclusive (Salomonsen, 1992), indeed, they are 

mutually supportive. Their merits and demerits are not 

the concern here, save that it is important to emphasize 

that each relates the strong and consistent patterns 

of community organization to the sum of the 

behaviors of individual organisms. Analogizing them 

to the flow of energy and to the rate of its biological 

dissipation that their activities represent, however, 

recognizes openly the finite capacity to buffer contingent 

variability in the energy sources. Some preliminary 

estimates of their probable ranges are noted in 

Table 1. 

The effect of external forcing 

on community assembly 

It is rather obvious that few planetary ecosystems 

carry either the biomass or the structural complexity 

for which the foregoing passages argue, or achieve the 

ultimate autopoetic goal of stability. The override of 

resource scarcity as a causal impediment is generally 

recognized and the systemic reaction to eutrophication 

and its reversal make it an issue for consideration 

here. The override caused by frequent exceedence of 

buffer capacity is also well accepted but the relationship 

between external forcing and internal exergy 

needs to be clarified first. 

We have already shown that the assembly of an 

energy-harvesting network provides a capacity for

10 


exergy gain but that it is not a guarantee of income. 

For our Chlorella-based system, for example, seasonal 

changes in water temperature and daylength have 

deep-reaching impacts upon anabolic capacity. Even 

day-to-day changes in the intensity of the incident 

radiant flux, owing to atmospheric absorption and 

cloud backscattering, will affect the output of new 

Chlorella carbon. While the biomass is small and the 

immediate energy requirements of Chlorella are easily 

saturated, the assembly potential is hardly at risk, 

but a developing biomass raises the absolute maintenance 

demand and, so, increases its sensitivity to fluctuating 

light income. Of even greater importance to 

aquatic phototrophs is the fact that water absorbs light 

(even in the clearest lakes and oceans, light scarcely 

penetrates to beyond 120 m from the surface). This 

means that the depth to which phytoplankton are 

entrained beyond the water surface by turbulent diffusivity 

subtracts exponentially from the supportive 

capacity. Given the absorption coefficient in pure 

water averages about 0.17 m –1 over the visible spectrum 

and exceeds 0.4 m –1 in the spectral region of 

chlorophyll excitation (Kirk, 1994), it is easy to 

appreciate the diminution in chlorophyll-carrying 

capacity represented by increasing water-column 

depth. Deep entrainment has long been recognized to 

be the major limitation to the initiation of vernal 

phytoplankton blooming in temperate latitudes 

(Sverdrup, 1953), a finding which modern treatments 

have continued to uphold (Szeligiewicz, 1998; 

Huisman et al., 1999). Models using general calculations 

of photosynthetic potential, corrected for respiration, 

are available which predict the chlorophyll carrying 

capacity of water columns as a function of their 

entrainment depth and background light extinction 

(see Reynolds, 1997, 1998; Steel and Duncan, 1999). 

Even under the assumptions of incoming irradiance 

used to construct Figure 1, the supportive capacity of 

a water column of 80 m depth is less than 80 mg 

chlorophyll m –2 (i.e.,


result. If the conditions are sufficiently persistent, that 

is, for the equivalent of two to four generation times 

(see Reynolds et al., 1993), the species composition 

alters significantly, heralding a community response 

(called “shift”; Reynolds, 1980) to weather-forced 

environmental change, and from which ready reversion 

is impossible. A new structure establishes, 

around a new attractor, pending at least another externally 

forced shift. 

The crucial issue is that while a structural 

response, or disturbance, is often clearly related to a 

critical forcing event or threshold, the lack of a significant 

reaction to a forcing should be seen as a positive 

result of its accommodation by resilient buffering. 

Either the difference between harvest and cost is such 

to absorb the forcing; if it is, the harvesting ability 

provides sufficient exergy to resist the variability in 

the flux. If it does not, harvesting rate suffers but the 

apparatus remains sufficiently intact to recover its full 

function in the event of suspension of the forcing. If 

the forcing persists, the buffering resilience moves to 

the adaptability of the cells. If this too is exceeded, 

resort moves to the population resilience. Ultimately, 

once this too is exhausted, the species composition of 

the phytoplankton itself becomes liable to alteration. 

These various responses to external forcing are 

represented in Figure 2. It is emphasized that the 

intensity and duration of forcing have to be viewed 

separately from the response. The severity of forcing 

must be measured against the scale of the exergy 

buffer and the resilience this provides to the biological 

structure to allow it to bounce back, once the forcing 

abates. However, if the forcing persists beyond the 

capacity of the exergy, then the non-sustainability of 

energetic outgoings in excess of harvested income 

leads to a rapid and downward revision in the biomass 

that can be carried. Mass is lost, information is sacrificed—the 

clear symptoms of disturbance (sensu 

Pickett et al., 1989). It is also plain that frequent alternation 

(say, at the scale of four to eight generations 

(a) 

(b) 

Figure 2(a). Representation of ideal system ascendency, in which community assembly works at the limit of the exergy flux. (b) In 

variable systems, ascendency is regulated by fluctuations in the harvestable energy income, which can be followed through time (broad 

arrow) and from time to time, falls below a level that sustains the maintenance demand; the necessary restructuring constitutes a 

disturbance, beyond the acquired structural resilience.

12 


times) between instances of severe forcing and with 

the more quiescent interludes that provide opportunities 

for restructuring, hampers the progress of an 

ecosystem towards energetic steady state. The deduction 

is no more than a restatement of Connell’s (1978) 

Intermediate Disturbance Hypothesis. 

The effect of resources on community 

assembly 

The second main impediment to the achievement 

by a developing ecosystem of its energy-determined 

potential is the premature exhaustion of the supply of 

one or other of the component elements. The effects 

of nutrient sparsity are well-recognized in aquatic systems, 

where sharp physical boundaries, across which 

exchange rates are defined or measurable, together 

with a well-developed understanding of the nutrient 

chemistries and a suite of sensitive analytical techniques, 

all contribute to the relative ease of determining 

availability and demand. Moreover, the composition 

of protoplasm is broadly similar among bacteria, 

protists, plants and animals: to be able to assemble 

biomass requires the delivery of up to twenty elements, 

some of which are much harder to attain, relative 

to requirement, than the others. For instance, this 

consistent cell stoichiometry indicates that in order to 

build as much as 50 g cell C m –2 requires the assimilation 

of an average of 1.2 g P, 9 g N and 0.05 g 

Fe m –2 . For various reasons (electrochemical binding 

to oxides, chemical inertia, insolubility), natural 

hydraulic exchanges are rarely able to maintain appropriate 

fluxes of all three elements. The affinity of the 

uptake mechanisms of the primary producers is usually 

very well-developed, and able to reduce the 

bioavailable sources to very low levels, indeed. In the 

plankton, at least, if bioavailable nutrient can be 

measured, primary production is not limited. Among 

temperate freshwaters, phosphorus exhaustion is a 

frequent limiting constraint, whereas nitrogen is generally 

supposed to be relatively the scarcest in lakes of 

tropical and arid regions, as well as in the coastal and 

shelf waters of the sea. In the Pacific Ocean, where 

concentrations of N and P are also habitually quite 

low, experiments have nevertheless revealed that iron 

maintains the critical limit (Martin and Fitzwater, 

1988). 

One or other of these nutrients then sets a maximum 

carrying capacity on the system, which in terms 

of active biomass, can be marked as an offset on the x 

axis of Figure 1 in order to re-define the shape of the 

exergy buffer (now reminiscent of the sail of a dhow 

or, perhaps, a windsurfer; see Figure 3a). The new 

boundary reduces the limits within which biomass 

assembly occurs but leaves the supportable limit with 

a margin of exergy. As is true of the other two margins, 

proximity of the system coordinates to the 

resource limit is indicative of strong selection for 

appropriate adaptive traits. However, the identity of 

the species to which any advantage may accrue will 

depend on just which resource it is that is deficient. 

Exhaustion of bioavailable silicon (which is not a 

problem for most non-diatoms) would favour a different 

outcome from exhaustion of bioavailable nitrogen 

compounds (which is, supposedly, no bar to the 

growth of dinitrogen-fixing Cyanobacteria). However, 

for other resources, such as phosphorus, the requirement 

is universal and the dependence upon an appropriate 

source is common. The crucial property in 

which planktonic algae vary is in their uptake affinity 

for the resource at very low concentrations. In 

essence, a high affinity permits one species to be able 

to gain more of its immediate resource requirement 

than its competitors, thereby permitting it to sustain 

its growth for longer, to add to its own potential exergy 

flux and, in all probability, to build a larger standing 

population than its rivals. Crucially, this may also 

mean an enhanced ability to recruit future populations 

when suitable conditions return, because a relatively 

greater number of propagules are poised to exploit the 

next growth opportunity. So it is that dominant organisms 

frequently match, and are indicative of, the habitats 

in which they occur preferentially. 

For examples of superior affinity, one need look no 

further than the classic experiments of Tilman and 

Kilham (1976) or of Rhee (1978). These revealed how 

much “further” certain algal species perform than others 

when low external nutrient concentrations obtain. 

Interestingly, the adaptations tend to be nutrient-specific, 

so that being able to operate on lower phosphorus 

concentrations than can other species is in no 

sense a guarantee of being able to work also at low 

concentrations of, for example, silicon or nitrogen. In 

fact, there is often an element of “trade-off ” with specialists 

in, say, silicon uptake having a relatively weak 

affinity for phosphorus, and vice versa. The key to 

this behavior is presumably the number of ion-specific 

acceptor sites per unit of biomass and the level to 

which the internal transport and assimilation can fall 

before the cell recognizes its own nutrient deficiency 

(see Mann, 1995). In practical terms, the behavior is 

revealed in the specific values of the vectors characterizing 

the Michaelis-Menten description of uptake 

dynamics. The higher is the value of maximum uptake


Figure 3(a). The imposition of limited resource availability on the upper attainable biomass. (b) Eutrophication is the abrupt rightward 

displacement of this limit; conversely, restoration measures are designed to bring about an equally abrupt leftward move: a delay in this 

response is perceived as a form of resourcing resilience. 

rate V U max and the lower is the concentration K U 

required to half-saturate it (both properties favoured 

by having a large number of receptor sites), then the 

lower is the external concentration, S, at which V U 

can continue to meet the demands of growth. 

As more has been discovered about the needs of 

plankton autotrophs, or, rather, about how well they 

are adapted to secure their needs under conditions of 

severe supply difficulties, quite remarkable differences 

in affinity have been diagnosed. The sketch in 

Figure 4 is purely illustrative, purporting to represent 

the extremes of tolerance of Microcystis aeruginosa 

to very low concentrations of carbon, of Planktothrix 

agardhii to very low light doses and of the mixotroph, 

Synura petersenii, to very low external concentrations 

of bioavailable phosphorus. There are more benign 

conditions of light and nutrients where all three are 

able to coexist with each other and, also, with a large 

number of more generalist species. The point that 

must be emphasized is that specialist adaptations are 

likely to be advantageous to a species when the environment 

does become highly selective when one or 

other of these deficiencies begins to impose severe 

restrictions upon the abilities of most species to operate 

at all. 

As with carbon dynamics, the principle of resource 

limitation is not confined to the primary producers. 

The importance of the concentration of adequately 

nutritious and edible planktonic foods (including suspended 

fine detritus and bacteria as well as phytoplankton) 

to the ability to support a primarily filterfeeding 

zooplankton, rather than one of microprotists 

and calanoid raptors, is generally recognized (about 

0.1 g C m –3 : Thompson et al., 1982). The dietary 

requirements of facultatively planktivorous fish are 

also sufficiently well-known (e.g, Elliott and Hurley, 

1999) for it to be deduced that concentrations of 

zooplankton equivalent to

14 


Figure 4. Diagram to show the environmental growth tolerances 

of three species of phytoplankton (Microcystis aeruginosa, 

Planktothrix agardhii, Synura petersenii) with respect to photo 

flux, phosphorus and carbon availability. 

also be noted that the exergy buffer narrows at higher 

levels of biomass and, so, increases the vulnerability 

of enriched, productive systems to disturbance by 

external forcing. In the relatively recent instances of 

anthropogenic eutrophication (within the last 30–50 

years), there has been rapid filling of the added capacity 

by the larger biomass of the existing biota—or 

more of the same. Primary biomass is added ahead of 

development of the new information represented by 

an appropriately modified consumer community. In 

the terminology of Ulanowicz (1986), eutrophication 

is a form of ascendancy in which added throughflow 

exceeds the addition of new information in the flow 

network. It could be said that the biological system is 

not yet sufficiently organized to allocate the new 

resources in the most efficient structures. Until the 

additional resource can be absorbed into the biomass 

of long-lived, resource-efficient (classically K-selected) 

organisms, it remains labile and exchangeable 

among the storages provided mainly by short-lived 

(classically r-selected) organisms. Much of the additional 

resource oscillates among water, sediment and 

transient biomass, whereas the more desirable alternative 

is for it to be invested in the flora and fauna of 

a macrophyte-dominated system (Scheffer and 

Jeppesen, 1998). In its planktonic state, the luxury 

labile capacity buffers the resource fluctuations in a 

manner analogous to the exergy riding on the energy 

exchanges of the system. 

Considering now the impact of a lake therapy 

achieved by the abrupt reduction of the external loading 

of the limiting resource, the desired result is a 

similarly abrupt system response represented by the 

leftward shift of the resource barrier. Of course, this is 

not achievable while resource capacity is still being 

freely exchanged among the system components. As 

has been pointed out in Sas’ (1989) analyses of 

schemes based upon reduced phosphorus loading, the 

truncation of resources is effective in deeper (mainly 

>5 m) systems where the sediment phosphorus is generally 

closed to effective recycling. That part of the 

extrabiotic pool which does not remain in the sediment 

store is progressively flushed from the lake: the 

generality that this takes the equivalent of three 

hydrological retention times before a new steady state 

is achieved is probably well-testified (author’s unpublished 

assessments). However, in shallow lakes 

wherein most of the sediment is reached by wind- and 

wave-generated shear stress, recent sedimentary fractions 

are retained in the extant internal cycling and, 

thus, the resource buffering role is prolonged. 

Eventual rundown is predictable but the time scale 

depends both on the margin of excess and the recycling 

frequency. Not all lakes have proved as 

intractable as Søbygaard (Søndergaard et al., 

1993) but this merely represents one of the fullest 

studied and most impressive examples of resilience to 

limnotherapy designed to reverse the effects of 

eutrophication. 

By extension of the analogy to the exergy buffer, 

resilience to restoration measures is explicable as a 

function of the buffer provided by extrabiotic resource 

pools in the system. Variations in the resource constraint 

through what is, in effect, a short period in the 

life span of a water body are damped by the geochemical 

exchanges involving parts of the system 

other than the respondents in question. 

Conclusion: Resilience 

and ecosystem health 

However far we attempt to compare and to interrelate 

the analogies between two distinct limnological 

connotations of resilience, the dilemma identified


in the opening two paragraphs persists: one of them is 

supposed to be good (for stability, functional recovery, 

maintenance of diversity); the other seems rather bad 

(for cost-effective management, knowledge vindication, 

remediation return). Clear distinction between 

the two intents is still necessary: pending further discussion, 

I nominate as provisional terms, structural 

resilience” and “resourcing resilience”. 

Although the present forum was convened for 

resilience in the resourcing sense, there seems to be 

no good case to be made for continuing with this 

usage. As a scientific issue, it is no more than a hysteresis 

effect, brought about through the biological 

separation of the response (lower plant biomass) from 

the driving variable (reduced external nutrient loading). 

As an economic issue, a difficulty persists insofar 

as investment of large amounts of public or customer 

money in restoration projects must show 

results. Yet that has always been so—it is not sufficient 

to produce an obfuscation of limnetic insensitivity 

to restorative therapy by attributing it to resilience. 

Blaming delay on insensitivity might make a better 

case for researching, and quantifying, the sensitivity 

of systems before the therapy is applied so that some 

better anticipation of its likely efficacy and value can 

be given at the start. 

Against this, structural resilience is a variable 

property of all ecosystems and, I suggest, provides a 

dimension for checking on their condition, or the state 

of their ecosystem health. If I cannot satisfy the 

forum’s wishes, perhaps it would support the organizers’ 

cause to fit structural and resourcing resilience 

into a general view of the diagnostics of ecosystem 

health. The term is still quite young and, to many ecologists, 

lacks a precise and widely-accepted meaning. 

For some, healthy is synonymous with oligotrophic 

and pristine; lakes that comply with the latter two 

adjectives may well be healthy but a presupposition 

that everything else is therefore unhealthy is erroneous 

and dangerous. The criterion of ecosystem 

health should be its sustainability—that is, its ability 

to maintain its structure and function in the face of 

(a) 

(b) 

Figure 5. (a) Axes for representing ecosystem health as proposed by Costanza and Mageau (1999), corresponding to 

resilience (x axis), structure (y axis), and productivity (z axis). The axes are used in (b) to distinguish assembly tracks 

of an ascendant ecosystem with increasing resilience, structure and productivity from one undergoing rapid eutrophication, 

where productivity and resilience increase, but structural information is deficient.

16 


stress (Costanza and Mageau, 1999). Is this not just 

another way of asking how much structural and 

resourcing resilience the system has 

In the same way that systems emerge only through 

the aggregate of the behaviors of individual organisms 

of discrete populations, so system health should be 

manifest in the representation of key indicator species 

and functional types. Thus, the indices of ecosystem 

sustainability promoted by Costanza and Mageau 

(1999) provide a ready framework of dimensions for 

distinguishing “good” from less desirable resilience. 

The lines in Figure 5a represent the axes of a threedimensional 

plot, which share a common origin at 

“0”. The z-dimension corresponds to specific processing 

rate (representing gross primary production, 

foraging rate, oxidative capacity); the y-dimension 

corresponds to structure (representing greater species 

richness and interconnectance, more information 

organized in more biological complexity). These two 

axes define a (y-z) plane analogous to that used by 

Reynolds (1999) to distinguish lakes on the basis of 

their metabolic properties. The third (x-) dimension 

corresponds to resilience as a separate measure of 

ecosystem behavior. It was not qualified by Costanza 

and Mageau; their intention is the “structural” context 

but it holds for “resourcing” too. In Figure 5b, two trajectories 

of ecosystem development are inserted into 

the three-dimensional space created by the axis 

boundaries. For simplicity, they refer strictly to the 

early development and they do not show symptoms of 

disturbance. Both trajectories commence close to the 

origin and, quite properly, demonstrate high biomass 

production. Both trajectories acquire resilience, also 

as anticipated but one of them also rises with respect 

to the y axis, as it gains more species, develops more 

network connectances and becomes more complex. 

This is the trajectory of Ulanowicz’ (1986) ascendancy 

and the resilience it builds is of the structural kind. 

The other trajectory advances in productivity and processing 

but the biomass investment is in a few specialist 

species, not in structural diversity. Managers 

will attest to its high resilience to therapy. The second 

trajectory acquires resourcing resilience but high 

diversity is delayed. It is productive but has low information. 

Presently judgement resides with the view that 

high-diversity, low-productivity systems are healthier 

than low-diversity ones, though high productivity is a 

characteristic of early successions and it is what 

makes them so attractive for exploitation. For 

exploitation to be sustainable, systems must always be 

allowed the resilience to recover their ascendancy. 

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