ECOLOGICAL MODELLING

Transcription

1 ECOLOGICAL MODELLING II. CONCEPTUAL AND MODELING THEORY 1 1. Models - Mankind has always used models as tools to solve problems as they give a simplified picture of reality - The model never contain all the features of the real system - The model contains the elements essential in the context of the problem to be described or solved 2 1

2 1. Models Comparisons : geographical maps vs. ecological models Geographical maps: - different types / different purposes (roads, land use, ) - different scales - don't contain all details Ecological models: - focus on the objects of interest for the problem under consideration - too many irrelevant details would cloud the main objectives of the model - very many different models of the same ecosystem Physical and Mathematical models - Physical models: model plants, pot experiements = micro cosmos models 4 2

3 1. Physical and Mathematical models - Mathematical models: describing the main characteristics of the system in mathematical terms Micro-cosmos models help to build mathematical models 5 1. Condition for modelling Conditions that enabled rapid development of ecological modelling in last 30 years: 1: Development of computer technology: - enabled to handle very complex mathematical models 2: General understanding of pollution problems: - complete elimination of pollution is not feasible ( zero discharge ) - pollution control requires serious consideration of pollution impacts on ecosystems 3: New knowledge about environment and ecological problems: - quantitative relationships between the elements in the ecosystems and between ecosystem and its environment 6 3

4 1. Models vs. Statistics Model: a synthesis of what we know about the ecosystem with the reference to the considered problem Statistical analysis: reveal of relationships between the data Model encompasses our knowledge about the system: - which components interact with each other i.e. zooplankton grazes on phytoplankton - mathematical formulation of the processes between the elements of the model - link between the problem and the process needed for the solution of the problem 7 1. Models vs. Statistics Model can offer deeper understanding of the system then statistical analysis. It may yield better management plan for solving the focal environmental problem. The results of statistical methods should not be ignored in the process of model making. Models are build on: - statistical analysis of data - physical-chemical-ecological knowledge - the laws of nature - soft knowledge (common sense) 8 4

5 - to understand the function of such a complex system - to survey the many components of and their reactions in an ecosystem The application of models in ecology is almost compulsory: ==>> investigation of complex systems ==>> solving complex problems ==>> the only approach to deal with irreducible systems Nuclear physic: properties of sub atomic particles are studied by models -Astronomy:life of the stars and galaxies are studied by models - Ecology: properties of ecosystems is examined with models 10 5

6 Modelling as an instrument to understand the properties of ecosystem. Advantages of models as useful tool in ecology: - instruments in the survey of complex systems - used to reveal system properties - reveal the weakness in our knowledge => used to set up research priorities -test of scientific hypothesis as the model can simulate ecosystem reactions which can be compared with observations 11 Use of models to test hypothesis => very often purpose of modelling PRO : => model about interaction between two or more variable is tested on additional several cases to increase certainty => if the relationship holds several examinations a wider scientific use of this model in possible 12 6

7 CONTRA : Using model (ecological model) to test hypothesis: 1.=> model is correct and the hypothesis is correct 2.=> model is not correct but the hypothesis is correct 3.=> model is correct but hypothesis is not correct 4.=> model is not correct and the hypothesis is not correct 13 The idea behind the use of models as scientific tools may be descried as an iterative development of pattern: 1. Both the model and the hypothesis are correct => new piece of the pattern is made 2. Does the piece fit into the general pattern? => NO --- go back: - change the model or hypothesis - forced to change the pattern => YES --- new piece can be used at last temporarily in the pattern which is used to: - explain other observations - improve our models and make other predictions, which are then tested 14 7

8 The model is tested in more case studies A developed model with acceptable results in several case studies More tests of the hypothesis by the model Test against the general theoretical pattern. Improved model Confirmation Further confirmation Hypothesis Elements in the theoretical pattern (ecosystem theory) The model is used to test the hypothesis in a few cases Models as a Management tool The idea behind the use of ecological management models: Industrialisation and urbanisation Emission ECOSYSTEM Environmental technologies Ecological modeling Reasoning for introduction of ecological modelling as a management tool in around

9 3. Models as a Management tool Modelling approach used today is more complex and more comprehensive (system approach) and respects: - Application of environmental technologies - Cleaner technologies - Point or no point (diffuse) source of pollution (agronomy) - Global warming, depletion of the ozone layer (1 st generation of models): -the model of the oxygen balance in a stream (the Streeter- Phelps model): - the pray-predator realtionship (the Lotka-Volterra model) Alfred Lotka Vito Volterra 18 9

10 (2 nd generation of models): population dynamic models, more complex river models (3 rd generation of models): use of ecological models in environmental management (first eutrophication models, very complex river models ): revolution in computer technology limitation of data and knowledge about ecosystems and ecological processes (structures and processes) 20 10

11 To develop sound ecological models we need knowledge about ecosystem well defined problem knowledge about ecological components quality data Modelers became critical in their acceptance of the models 21 They confirmed general recommendations for development of the models follow strictly all steps of the procedure (i.e. conceptualization, selection of parameters, verification, calibration, sensitivity, validation, etc.) complexity of the model must be in balance between data, problem, ecosystem and knowledge sensitivity analysis should be used in the selection of model components and model complexity parameters estimation by using all the methods: literature, measurements in lab or on site, calibration sub-model and entire model, estimation based on allometric principles, 22 11

12 (4 th generation of models): - relatively sound ecological bases - emphases on realism and simplicity - based on many case studies - development of ecotoxicological models (5 th generation of models) - development of structural dynamic models - development of modelling software tools (ECOPATH, STELLA, MATLAB) 24 12

13 (6 th generation of models) - induction of models from data (AI, machine learninig, data mining) - qualitative models 25 Conclusions: - Following recommendation it is possible to develop models that could be used as prognostic tools - Models based on lower quality data could give insight into the handled they are more simple and such are often of particular value - Models based upon ecological knowledge are powerful tools in understanding ecosystem behavior and as a tools setting up research priorities =>contribution to ecosystem theory and better environmental management 26 13