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1/ INTEREST meeting Rothamsted,October 2004 Indicators and models: Tools for ecosystems management Margarida Cardoso da Silva LNEC-Lisboa.

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Presentation on theme: "1/ INTEREST meeting Rothamsted,October 2004 Indicators and models: Tools for ecosystems management Margarida Cardoso da Silva LNEC-Lisboa."— Presentation transcript:

1 1/ INTEREST meeting Rothamsted,October 2004 Indicators and models: Tools for ecosystems management Margarida Cardoso da Silva LNEC-Lisboa

2 2/ Structure of the presentation Need of management Management methodology Instruments  Indicators - Concepts, variables Evaluation criteria Conceptual models Building indicators  Models – concept, objectives, types, variables Building a model - method Examples

3 3/ Need for management Growing concern on use of natural resources Sustainability relies on strategic management Management goals:  Protection – to preserve human investment and defence against accidents and natural disasters.  Conservation – to guarantee the preservation of physical and biological domains, productive and diverse with an intrinsic value as ecosystem  Resource management - to harmonize conflicting and competitive uses

4 4/ Management Methodology  General methodology, function of: The type of ecosystem The issues to address  Includes a phase of: Planning, Implementation Revising.  Uses management instruments Revision of the plan InstrumentsStrategies Strategic objectives Sustainability Goals Operational objectives Performance evaluation Operational plan Definition of priority of action Objectives Identification and analysis of problems

5 5/ Management instruments Regulatory - institutional measures  Political  Legal  Administrative Economic - the existence of financial stimuli and the possibility of voluntary action Technical and scientific - rely on scientific knowledge  Environmental indicators (characterization, monitor progress and effects of management action)  Modeling techniques Diagnosis - support understanding of processes Prognosis - forecast effects of changing pressures on the state of the ecosystem  Geographical information systems ( geo-referenced representations of the ecosystems, of their context and uses, and of their characteristics

6 6/ Basic concepts Environmental indicators Basic concepts “…a descriptor of the pressures on the system, of its state and the changes of the system. Or,: …as quantitative or qualitative measures that can be assessed in relation to a criterion that describes features of an ecosystem or the related social system, or described elements of prevailing policy and management conditions and human driven processes indicative of the state of the eco-and social system.  Organized in a conceptual framework  Associated with evaluation criteria  Associated with a temporal and spatial domain

7 7/ Basic concepts Environmental indicators Basic concepts  Parameter - environmental variable that can be measured or to wich can be associated a numerical value relevant to the characterization of the environment  Environmental indicators- sets of parameters or of values derived from them relevant to the characterization of specific aspects of a certain region or of its evolution.  Indices - numerical values calculated from the environmental variables that aim to represent in an only numerical value, a certain generic aspect of the environmental state of an ecosystem.

8 8/ From variables to indicators Types of variables to build indicators  State variables, in a generalized thermodynamic sensu, which are stocks e. g. of mass or energy.  Fluxes, as measures of variation of state variables or of the controlling forcing functions.  Ratios between state variables, between fluxes and between state variables and fluxes.

9 9/ Target audience Environmental indicators Target audience Total amount of information Growing data processing Indicators for public Indicators for managers Indicators for scientists

10 10/ Evaluation criteria Environmental indicators Evaluation criteria Reference value What guarantees the sustainability of the system Minimum anthropogenic interventions - pristine conditions Defined on the basis of scientific knowledge Objective value Considers scientific knowledge Socio economic and management factors  The selection of evaluation criteria is complex and very relevant  Effects on the final judgment

11 11/ Summary of properties Environmental indicators Summary of properties Allow the organization of information in a systematic way Sensitive to action (information, management, actions) Are associated with criteria and objectives Understandable by wide audiences

12 12/ Conceptual framework Environmental indicators Conceptual framework State of the Environment Water Sediments Biota Human Activity Resources (use) Emissions Energy Agriculture Industry Economic Environmental Agencies Administration Private Pressures State Response Responses from Society (action) Human Activity Energy Agriculture Industry Driving-forces Impacts Values Public Health Biodiversity Functions Recreation Fisheries Food Macro economic policies Prioritize action

13 13/ Examples of application Environmental indicators Examples of application European Union - European Environment Agency State of the Environment Report Pressure indicatorsState indicatorsImpact indicators Uses Population densityMicrobiological contamination Density of recreational activitiesOrganic matter Infra-structures Emissions Nutrients (N, P) Concentration of N-NO 3 Eutrophication Red tides OilVisible films Metals (Cu, Cr, Cd, Hg, Pb, Ni, Zn)In sediments and biota Organic micropolutantsIn sediments and biota

14 14/ Examples of application Environmental indicators Examples of application US-Environmental Protection Agency 1st order:Changes in behavior. Evaluate the need of action 2nd order:Pollution abatement. Evaluate the reduction of pollution loads as a consequence of management/policy action 3rd order:Water and sediment quality. Inform about the degree of the changes 4th order:Biota and habitat. Provide information about the extension of the changes in the health of ecosystems and organisms and on the economy of the region

15 15/ Building a system of indicators (1) Issues identification Conceptual Model Seleccion of variables Data base Uses and functions of the ecosystem Users Quality objectives Legislation and norms

16 16/ Building a system of indicators Environmental indicators Building a system of indicators Issues Relevance criteria  Legal-economic  Ecologic  Public health  Data availability Examples:  Eutrophication  Oxygen content  Xenobiotics  Habitat integrity  Economic output Types of variables Context information  Morphology  Hydrology Pressure  Susceptibility  Loads of matter and energy  Accidents State  Presence /concentrations of substances Response  Operational, structural

17 17/ Building a system of indicators Environmental indicators Building a system of indicators Significant values Evaluation criteria Values of indicators Data base Space domain Morphology Hidrology Reference Objective Classes of quality Temporal domain Temporal scales Graphic and Algebraic algorithms

18 18/ Building a system of indicators Indicators Test values  Dimension removal Normalized Indicator  Algebraic operador  Penalty curves Quality classes Rules of agregation Integration Spatial – homogeneous zones  Morphology  Salinity  Management / uses Temporal – time scales  Seasonal  Moon

19 19/ Models as management tools...in science, a model has the objective to understand and represent, even partially, reality. Models are about:  “discovery/understanding”.  Behaviour  Models are at the same time true and not true.

20 20/ Models as management tools Models Dynamic representation of the influence of controlling factors and of inter-relationships between state variables Objectives of modelling:  Provide a representation of reality  Uncover relationships between variables (state and forcing functions) –better understanding of the system - diagnosis modelling  Forecast values of variables and of the behaviour of the system - prognosis modelling

21 21/ Modelling process Conceptual model – tell the story we know about the system.  Identify: Interdependencies Influence of external factors Time scales Boundaries (what is part and is not part of the system)  Verbal  Schematic (blocks diagram)  Symbolic language (Odum, Forrester)

22 22/ Modelling process Variables  State variables – stocks  Forcing functions / driving forces- external  Fluxes of matter and energy  Rates – control fluxes  Support variables

23 23/ Conceptual model River Area Water level Volume Paddy field Flux Poim_river Flux River_poim Flux Poim_fields Flux Fields_poim POIM Area Water level Volume Driving forces Tide Fresh water flow Control function Desired level inside the khazan

24 24/ Conceptual model River Salinity Paddy field Salt flux Poim_river Salt flux River_poim Salt flux Poim_fields Salt flux Fields_poim POIM Salinity Driving forces Estuarine salinity

25 25/ Symbolic languages

26 26/ Symbolic languages State variables – stocks Auxiliary variables Constants Fluxes of information Fluxes of matter and energy

27 27/ Modelling process (2) Define mathematical relations Identify time units (depending on time scales of processes) Choose values for:  Initial conditions of state variables  Parameters  Constants Start simulations Check obvious errors (e.g.- stop river flow and volume variation does not respond) Analyse results – calibrate-validate

28 28/ Conceptual modelling Schematic representation

29 29/ Formulation System of equations

30 30/ Ecological models

31 31/ Final remarks (1) Different management instruments are not mutually exclusive Have the potentiality to be used in ecosystems of different nature (agro- forest, production forest, aquatic) as diverse are the case studies of the INTEREST Project. Selection of indicators is a starting point to identify model variables

32 32/ Final remarks (2) Modelling exercise contributes to identify relationships between variables An initial understanding of the relevant process is needed to allow the development of any of the types of tools No useful conclusions without a basic set of data


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