Application of Indicators f th A t ffor the Assessment of

Ecosystem HealthEcosystem Health

By S. E. Jørgensen, DFU, Environmental Chemistry Universitetsparken 2 2100 pCopenhagen Ø, Denmark

sej@dfuni dksej@dfuni.dk

The presentation will cover:

• Various answers to the question: what is ecosystem health?ecosystem health?

• Which indicators to apply?Whi h t f EH th ?• Which aspects of EH can they cover?

• Special emphasis on Odum,s attributes d h li ti i di tand holistic indicators

• Conclusions about the use of indicators ith h i ifiwith emphasis on exergy. specific exergy

and buffer capacities as EHI?

Important book:

• Jørgensen, S.E., Robert Costanza and Xu Fuliu Handbook of Ecolgical IndicatorsHandbook of Ecolgical Indicators for the Assessment of Ecosystem Health, crc, Lewis Publ 2004Publ. 2004.

Do you know the disease?

• The disease may define• The disease may define the indicator, for instancethe indicator, for instance PCB pollution in the Great Lakes

• Invasion by zebra mussels

25

µg / g ww in birds

25

20

1515

1010

5

1974 2000

V B t l ff (1950) h t i d th l ti f lVon Bertalanffy (1950) characterised the evolution of complex systems in terms of four major attributes: 1) progressive integration (entails the development of integrat1) progressive integration (entails the development of integratlinkages between different species of biota and between biota, habitat and climate), 2) progressive differentiation (progressive specialisation as syevolve biotic diversity to take advantage of abilities to partitionreso rces more finel and so forth)resources more finely and so forth),3) progressive mechanisation (covers the growing number of febacks and regulation mechanisms),backs and regulation mechanisms),4) progressive centralisation (it does probably not refer to a centralisation in the political meaning, as ecosystems are charsed by short and fast feed backs and decentralised control, bumore and more developed (synergistic) cooperation among theorganisms (the Gaia effect) and the growing adaptation to all oorganisms (the Gaia effect) and the growing adaptation to all ocomponent in the ecosystem).

What should ecologicalWhat should ecological indicators cover?indicators cover?

• l) homeostasis; ) ;• 2) absence of disease, • 3) diversity or complexity; • 4) stability or resilience;• 4) stability or resilience; • 5) vigor or scope for growth5) vigor or scope for growth • 6) balance between system

components

Two methods may beTwo methods may be applied:applied:

B Di t M t /• By Direct Measurements / Observations MethodObse at o s et od

• By the Application of Models, f i ECOPATHfor instance ECOPATH or a bio-geochemical modelbio geochemical model

Classification of EcologicalClassification of Ecological IndicatorsIndicators

• Reductionistic (single) indicators - like for instance PCBinstance PCB

• Species present / absentS ih li ti i di t f i t Od ’• Semiholistic indicators: f.inst. Odum’s attributesH li ti i di t bi di it /• Holistic indicators - biodiversity / ecological network“S h li ti ” th d i• “Super-holistic” - thermodynamic indicators

Present and absent ofPresent and absent of species;species;

• ANN has been applied on big data bases to find relationships between species present / absent and water p pquality in rivers: for fish species, for diatom species and for species ofdiatom species and for species of benthic fauna. The present of species have been applied asspecies have been applied as indicators.

Three Growth Forms:

1. Growth of biomass- the biophysical structure

2 Growth of network (feed backs)2. Growth of network (feed backs)3. Growth of information (from r to K

t t i t f ll t bistrategists, from small to bigger organisms, from slightly developed to more developed organisms)

E.P. Odum’s attributesEarly stage system:GF1 P/B hi h (B i l )

Mature system:P/B hi h (B i hi h)GF1:P/B high (B is low)

GF1:R/B highGF2 F f d b k

P/B high (B is high)R/B is lowM f d b kGF2:Few feed backs

GF3:Low biodiversityMany feed backsHigh biodiversity

GF2:Simple networkGF2+3:Low buffer

it

Complex networkHigh buffer capacity

capacityGF3:Few ecological

niches

Many ecological nichesK-strategists

nichesGF#:R-strategistsAll GF:Low exergy

High exergy

EELS 11.0

ADULT PREDATORS 6.1

YOUNG PREDATORS 11 5 11.5

ZOOPLANKTON 267

SILVERSIDES

ADULT MUGILIDS 9 2

9.1

ADULT MUGILIDS 9.2

BENTHOS 13.1

MOLLUSCS 477

PHYTOPLANKTON 15.0

BENTHIC PRODUCERS1071

DETRITUS 5000

Ascendency is a networkAscendency is a network measuremeasure

• Higher flow of energy• Higher flow of energy through the system higher ascendencyMore complex network flow• More complex network flow, higher exergyg gy

• Ascendency and exergy are l t d f d lcorrelated for models

SUN1000 100 10 1

kJ / m2 h

SUN

Solar Equivalents kJ / m2 h

1000SUN

1000 1000 1000

E T f ti R tiEnergy Transformation Ratios = Embodied Energy Equivalents. kJ / m2 h

1 10 100 10001 10 100 1000SUN

Biodiversity is determinedBiodiversity is determined by the use of :by the use of :

• The number of speciesp• Shannons index =∑pi * ln pi∑p p

Results of differentResults of different indicators by EHA on 60 y

Italian and Chinese Lakes

Si l i di• Single indicators• Semi-holistic indicatorsSemi holistic indicators• Biodiversity• Thermodynamic indicators: exergy,

specific exergy and buffer capacityp gy p y

Dynamics in lake trophic states

R i di t Oligo- Eutrophication—Responses indicators Oligoeutrophication

—Eutrophication

EutrophicationHyper-

eutrophication

Phytoplankton cellnumber a,b Increase IncreasePhytoplankton cell number

Phytoplankton bio mass (BA) a,b Increase Increase

Phytoplankton cell size a,b Increase Increase

Phytoplankton diversity a Decrease Decrease

Zooplankton biomass (BZ) a,b Increase Decrease

Zooplanktonbod si e a,b Decrease Decrease

Structuralresponses

Zooplankton body size , Decrease Decrease

Zooplankton diversity a Decrease Decrease

BZ/BA ratio a,b Decrease Decrease

BZmacro./BZmicro. Ratio a,b Decrease Decrease

Phytoplankton primaryproduction a Increase Increaseproduction

P/B ratio a ≈ 1 < 0.5Functionalresponses

P/R ratio a ≈ 1 < 1.0

Exergy a,b Increase DecreaseSystem-level

responses Structural exergy a,b Decrease Decrease

4 The ecological indicators for lake ecosystem health assessmentRelative healthy state

Ecological indicators Good Bad

Methods for indicator values

St t l 1 Ph t l kt ll i S ll L MStructuralndicators

1. Phytoplankton cell size2. Zooplankton body size3. Phytoplankton biomass (BA)4 Zooplanktonbiomass (BZ)

Small Large Low

High

LargeSmallHighLow

Measure Measure Measure

Measure4 Zooplankton biomass (BZ)5 Macrozooplankton biomass (Bmacroz.)6 Microzooplanktonbiomass

High High

Low

LowLow

High

Measure Measure

Measure6. Microzooplankton biomass (Bmicroz.)7. BZ/BA ratio8 Bmacroz/Bmicroz ratio

Low

HighHigh

High

LowLow

Measure

CalculateCalculate8. Bmacroz./Bmicroz. ratio

9. Species diversity (DI) High

HighLow

CalculateMeasure & Calculate

Functionalndicators

10. Algal C assimilation ratio11 Resource use efficiency

HighHigh

LowLow

MeasureMeasure&Calculatendicators 11. Resource use efficiency

(RUE)12. Community production (P)13 P/Rratio

High Low ≈ 1

High

LowHigh

> or < 1Low

Measure & Calculate MeasureMeasure & CalculateMeasure&Calculate13. P/R ratio

14. P/B ratio15. B/E ratio

High High

LowLow

Measure & CalculateMeasure & Calculate

System-level 16. Buffer capacities ( ) High Low CalculateSystem levelndicators

16. Buffer capacities (_)17. Exergy (Ex)18. Structural exergy (Exst)

HighHighHigh

LowLowLow

Calculate Calculate Calculate

The ecological indicators and their measured values in different period in the Lake Chao(from April 1987 to March 1988)

Measured indicator values indifferent period**Ecological Relative order of healthpgindicators* A B C D E state in different period

(good _ poor)BA 4.5 1.31 21.82 0.60 0.58 E> D >B>A >CBA E D B A CBZ 0.33 0.34 1.76 4.15 13.54 E > D > C > B > A

BZ/BA 0.073 0.26 0.081 6.92 23.24 E > D > B > C > AP 1.42 1.38 7.03 0.74 0.21 E > D > B > A > C

P/B 0.292 1.053 0.322 1.233 0.363 D > B > E > C > ADI 1 59 1 62 0 28 1 83 1 97 E> D >B>A >CDI 1.59 1.62 0.28 1.83 1.97 E > D > B > A > CEx 112.0 98.5 606.3 1075.1 3350.9 E > D > C > A > BExst 25.33 52.8 48.0 213.6 238.6 E > D > B > C > A

β((TP)(Phyto.)) -0.014 6.45 0.04 0.92 -0.371 B > D > C > A > EComprehensive results E > D > B > A > C

* BA Ph l k bi ( 3) BZ Z l k bi ( 3) P* BA: Phytoplankton biomass (mg•m-3); BZ:Zooplankton biomass (mg•m-3); P:Algal primary productivity (gC•m-2•d-1); DI: Algal diversity index; Ex: Exergy(MJ•m-3); Exst: Structural exergy (MJ•mg-1); β((TP)(Phyto.)): Phytoplankton buffer

it t t t l h hcapacity to total phosphorus

** A: Apr. - May 1987; B: Jun. - Jul. 1987; C: Aug. - Oct. 1987; D: Nov. - Dec.1987; E: Jan. - Mar. 1988

System at temperature T, pressure p and the chemical potential µ(1)potential µ(1)

Exergy difference or gradient=

k d d b thwork produced by the gradient in chemical potential

Reference environment at same tempera- ture T and pressure p but by a chemicalture T and pressure p, but by a chemical potential at thermodynamic equilibrium (no free energy available, no gradients):µ[0)gradients):µ[0)

External factors Forcing functionsfunctions

EcosystemEcosystem structure at time t

New recombina- tions of genes / o s o ge es / mutations

Gene pool Selection

Ecosystem structure at time t +1t +1

07/09/03 4

H t l l t thHow to calculate the exergy• 1) Use models or other overviw of the• 1) Use models or other overviw of the

systems• 2) It can be shown that the following

approximation is valid Ex = ∑ ßici, pp ∑ i i,where ß is found from the embodied infomation Normalization ß = 1 basedinfomation. Normalization, ß 1 based on detritus (1 g detritus contains 18.7 kJ) ß for bacteria 3 8 for algae 18;kJ) ß for bacteria 3.8. for algae 18; jellyfs 118, worms 135. fish 499. Birds980, reptils 890,mammals 2130. The exergy found by this method will be

EXERGY INCREASES WITH:

• Growth form I: Increased biomassG h f II I d• Growh form II: Increased network (cycling and feed backs)( y g )

• Growth form III: Increased i f ti (k t t i tinformation (k strategists, regulation, genome)g , g )

Optimums

180

. .. .

Juvenile Regeneration

Optimum

Mixed

e nu

mbe

rs Juvenile

n, re

lativ

e

160

. Gap

estr

uctio

nEx

ergy

de

140

E

120

. Pasture

Exergy stored (GJ / ha)

1200 5 10 15 20 25 30 35 40

SPECIFIC EXERGYSPECIFIC EXERGY INCREASES WITHINCREASES WITH

• Growth form II:Increased network means better utilization of the exergyutilization of the exergy captured by the ecosysystem

• Growth form III: Increased in-formation means less exergyformation means less exergy lost and better cooperation (synergy)

Costanza (1992) summarises the concept definition of ecosystem health as follows:definition of ecosystem health as follows:health as 1) homeostasis (growth form II+III)1) homeostasis (growth form II+III)2) absence of disease (growth form I)3) diversity or complexity (growth form II+III))4) stability or resilience (buffer capacity isproportional with exergy)proportional with exergy)5) vigour or scope for growth and (all 3 growth forms)growth forms)6) balance between system components.( )(sp. exergy)

PROPERTIES OF EXERGY:

Increases with Measure:1) Distance from ther1) Biomass

2) The network (i.e.

1) Distance from ther-modynamic equili-brium) (

cycling, feed backs)3) Information (i e

brium2) Structure (biomassand network size) and3) Information (i.e.

regulation, size of

and network size) andfunction (information)3) Survival expressed as

ge-nomes, r->k

) pbiomass and information

strategists)

Table 2: Relationships between growth forms and goal functions

Growth FormI II III

____________________________________________________________

Exergystorage up up upgy g p p pPower / throughflow up up upAscendency up up upAscendency up up upExergy dissipation up equal equalRetention time equal up upEntropy up equal equalEntropy up equal equalExergy / Biomass=specific exergy equal up upentropy/biomass=entropy /biomass=spec. entropy prod. equal down downRatio indirect /di-

t ff t lrect effects equal up up______________________________________________________________

R2 5

2 0

2.5 +

2.0+

+

1.0

+

+

+

0.5 0 5 1 0 1 5 2 00.5 1.0 1.5 2.0

Log (N/P)

III. CONSERVATION

II EXPLOITATION

Trend of each further cycle

I. RENEWALIV CREATIVE DESTRUCTION

Specific exergy = exergy / biomass

The results of 18 EcopathThe results of 18 Ecopath models of marine systemsmodels of marine systems

• Exergy is correlated to the th h fl ( 2 0 975)through-flow (r2= 0.975)

• Exergy is correlated to R/B (r2=Exergy is correlated to R/B (r = 0.859)

• Exergy is correlated to B/P (r2 = 0 868)0.868)

• Specific exergy is correlated to p gythe number of species (r2= 0.843)

Eutrophication studies byEutrophication studies by modelsmodels

1200Exergy versus nutrients (mg /l)

800

1000

600

800

Ex TJ / l

TJ /

l

400Ex T

0

200

20100

nutrients

1,8

Structural exergy versus nutrients (mg /l)

1,7

1 5

1,6

E t TJ/TJ/g

1,4

1,5 Ex-struc TJ/g

Ex-s

truc

T

1,3

E

201001,2

nutrientsnutrients

Results from lakes 200000

y = 4222 9 + 2 5966x R^2 = 0 845

Exergy / total biomass incl. export

y = 4222,9 + 2,5966x R^2 = 0,845

100000 EXEX

06000050000400003000020000100000

biomass+exp

Structural exergy versus eutrophication, measured by the plant biomass + export

30y p p

2020

Struc exergyergy

10

Struc. exergy

Stru

c. e

xe

60000500004000030000200001000000

eutrophication

Coastal lagoon with clamCoastal lagoon with clam production and Ulvaproduction and Ulva

First figure exergy, second figure ifispecific exergy:

Full line: usual situationDotted line: Economic optimum cost /

benefit ulva removal / clambenefit ulva removal / clam production

Dashed line: Treatment of waste waterDashed line: Treatment of waste water discharged to the lagoon

Table 1 Empower density, exergy density and exergy/empower ratio for seven ecosystems

Control

Pond

Waste

Pond

Caprolace

Lagoon

Trasimeno

Lake

Figheri

Basin

Iberá

Lagoon

Galarza

Lagoono d o d agoo a e as agoo agoo

Empower density

(sej/year·l)20.1·108 31.6·108 0.9·108 0.3·108 12.2·108 1.0·108 1.1·108

(sej/year·l)

Exergy density1.6·104 0.6·104 4.1·104 1.0·104 71.2·104 7.3·104 5.5·104

(J/l)1.6 10 0.6 10 4.1 10 1.0 10 71.210 7.3 10 5.5 10

Exergy/empower

(J·year/sej) (x10-

5)

0.8 0.2 44.3 30.6 58.5 73 50.0

)

Buffer capacity defined as ∆forcing funtion / ∆state

i blvariable

• 1) It can be shown by models that the sum of many bufferthat the sum of many buffer capacities is proportional with exergy2) Notice that there is an• 2) Notice that there is an almost infinite number of buffer capacities

Use of buffer capacities inUse of buffer capacities in EHAEHA

• Use models to find relevant buffer capacities for instance ∆ h h / ∆∆ phosphorus / ∆ phytoplankton or ∆ dischargephytoplankton or ∆ discharge of ww / ∆ oxygen concentration in bottom water etcwater etc.

3

onun

ctio

2

cing

Fu

Forc 1

State Variable

Exergy and specific exergy have been applied as ecological indicators:g

1. By a comparison and health assessment of eutrophied lakes p2. By comparison and health assessment of coazones 3. By health assessment of Mondego Estuary in Portugal (Marques et al.)g ( q )4. By health assessment of Chinese lakes (Xu et 5. As ecological indicators for coastal lagoons ing gEurope (Jose, Zaldivar,ISPRA)6. For health assessment of different farming gsystems7. For health assessment in a situation where tox

contamination of ecosystems has taken place

Important holistic indicators

• Exergy and specific exergy• Important buffer capacities• Emergygy• Exergy/ emergy• Biodiversity• Biodiversity• Map of the network• Ascendency• Connectivity = the number of connection/ y

number of possible connections

How to consider severalHow to consider several indicators simultaneously?y

• By use of amoebeBy use of amoebe illustrations

• A few examples will be shown

C t dCase studyecosystem comparison

Beech ForestArable Land

E C t E t E tExergy Capture Entropy Export

BiodiversityMetabolicEffiency -1

Abi i Bi i W

ff y

Abiotic Heterogeneity

Biotic Water Flows

StN t i t L 1 StorageNutrient Loss -1

C it f S lfC it f S lf O i tiO i tiCapacity for SelfCapacity for Self--OrganisationOrganisation

Baumann et al. 2001

E C t E t E tExergy Capture Entropy Export

BiodiversityMetabolicEffiency -1

Abi i Bi i W

ff y

Abiotic Heterogeneity

Biotic Water Flows 50 %

Beech

StN t i t L 1

100 %

150 %

Beech Forest

StorageNutrient Loss -1 %

C it f S lfC it f S lf O i tiO i tiCapacity for SelfCapacity for Self--OrganisationOrganisation

E C t E t E tExergy Capture Entropy Export

BiodiversityMetabolicEffiency -1

Abi i Bi i W

ff y

Abiotic Heterogeneity

Biotic Water Flows 50 %

Beech MaizeMaize

StN t i t L 1

100 %

150 %

Beech Forest

MaizeMaizeFieldField

StorageNutrient Loss -1

C it f S lfC it f S lf O i tiO i tiCapacity for SelfCapacity for Self--OrganisationOrganisation

CONCLUSIONS:CONCLUSIONS:• Exergy and sp. gy p

exergy are consistent with the various definitions ofvarious definitions of ecosystem health

• They measure yecosy- system growth (three growth forms)forms)

• Practical applications show their usefulness

• They are not ffi i t b t tsufficient, but must

be supple-mented with other EHI