NGEN02 Ecosystem Modelling 2015

Introduction to ecosystem modelling • Concept of a system • From systems to ecosystems • Models and their use in science and research • System dynamics modelling

• Ecosystem modelling

Recommended reading: Systems and simulation models, Compendium page 3 Smith & Smith Environmental Modelling, Chapter 1

The system concept • ’Systems’ are fundamental to the organisation and processes of society and daily life  political system

 judicial system

 transport system

 booking system

 educational system

 heating system

Characteristics:

• impose structure, make things ’simple’, ’transparent’, ’efficient’ • ’make things work’ • consist of (clearly defined) elements ...

• ... and links between them

Political system

Transport system

Education system

The system concept • In science, systems provide a way of organising knowledge and ideas Characteristics:

• depict knowledge and ideas in a ’simple’, ’transparent’, ’efficient’ way • consist of (clearly defined) elements and links • have a clearly defined boundary

• omit extraneous detail, simplify / generalise

Related to the reductionist approach • defined fragment of knowledge or theory ... • ... reduced to its essential or most relevant (in a particular context) elements

System governing outcome of university studies parental expectations

external drivers

spare time system boundary

+ +

curiosity

+

+ Alcohol consumption

Study

+

+

system elements





+

+

existential worry

systeminternal feedbacks

+ Knowledge

state variable

+ Exam results

output

Exercise • In discussion groups 3-4 students, 15 mins • Think of and sketch a diagram describing a system from your daily lives, e.g. travel to work, doing the dishes, meeting a boy/girl. • Include and show  system boundaries  elements  sign and direction of links  state variable(s)  drivers and output(s)  at least one internal feedback  sign (+ or ) of each feedback loop

System governing outcome of university studies parental expectations

+ +

curiosity

spare time

+

+ Alcohol consumption

Study

+



+

+



+

existential worry

+ Knowledge

+ Exam results

System governing growth of a forest stand incoming sunlight

+ +

+

+ respiration

photosynthesis

leaf area index

+

external drivers

temperature





+

+

+

metabolic biomass

systeminternal feedbacks

+ Tree biomass

state variable

+ Harvestable timber

output

Summary: characteristics of a system A system: • a group of distinct but interrelated elements comprising a unified whole • may exhibit function arising from the interactions or links between elements

• may exhibit ’emergent’ properties or behaviour that can only be understood through the interactions and relationships of the elements of the system System element: • can be specified by its properties (e.g. a tree: height, leaf area, biomass) System state: • the value of an element (state variable) at a point in time

System function: • a time-dependent process linking two elements (e.g. leaf shedding transfers carbon between a tree and the soil) ... • ... or the aggregate effect of multiple processes and interactions (e.g. net primary production = photosynthesis  plant respiration)

From systems to ecosystems Ecosystems (Tansley 1935): • “Though the organisms may claim our prime interest ... we cannot separate them from their special environments, with which they form one physical system” • “The whole system … including not only the organism-complex, but also the whole complex of physical factors forming what we call the environment”

Operational definition:

boundary

• The organisms living in a particular place and a particular time, and the air, water and soil within which they live and with which they interact

elements

links

From systems to ecosystems Traditional conceptualisation: • System elements = pools or compartments of carbon, nitrogen, phosphorus, silica (major building blocks of life)

• System links = flows or fluxes of C, N, P, Si or energy between pools • Processes (biological, chemical, physical) control system dynamics by governing rates of fluxes. Processes+fluxes = ecosystem function. Ecosystem P cycle in Lake Tanganyika (Naithani et al. 2006)

compartment process flux

From systems to ecosystems Special characteristics: • Exhibit structure (spatial configuration of elements) at different scales • Driven by incoming matter and energy • Dynamic (evolve over time) governed by:

→ differential process rates → rate of incoming matter and energy → storage of matter or energy in ecosystem compartments

→ feedbacks between compartment sizes and process rates

Forest ecosystem C cycle NPP CO2

net ecosystem exchange CO2 net primary production

heterotrophic respiration

respiration

C flux

0 NEE

tree biomass C litter C pool litter+soil organic matter C

soil C biomass C time →

Forest ecosystem C cycle

uptake

release

*Hyvönen et al. 2007 New Phytologist 173: 463-480

Are ecosystems real or imaginary? • Like any system, an ecosystem is an abstraction (simplified representation) of the real-world thing it represents! • The degree and type of abstraction is defined by the choice of: → boundaries (physical/geographic/conceptual) → included elements (e.g. ”soil C” encompasses many compounds, organism groups and species) → included processes / fluxes / links / feedbacks → included drivers → representation of structure

The Scientific Method statistical test conclusion

?

true

false

test hypothesis

new knowledge

systematic observations

hypothesis

all knowledge

knowledge

observation

What is a model? In general: • An idealised, simplified or down-sized representation of something ... • ... the purpose is to describe, explain or depict the thing the model represents

What is a model? In science: • An idealised or simplified conceptual or formal representation of a phenomenon or item of interest, usually from the real world

• ... the purpose is to describe, explain or study the real-world phenomenon the model represents ... • ... enabling conclusions to be drawn about its properties or behaviour Part of the scientific method: • A model may be thought of as a formalised or explicit hypothesis about the real-world phenomenon under investigation • May be falsified by comparing its predictions to observational data. False model = rejected hypothesis

What is a model? May be very simple or general: • ”Helium is composed of two electrons bound by the electromagnetic force to a nucleus containing two protons along with one or two neutrons, held together by the strong force” ... or complex and mathematically explicit:

Choosing the right model for the right task

Smith & Smith Section 1.4

A model should be as simple as possible (for the task in hand) ... ... but no simpler

e.g. Light interception by a forest canopy

Uses of models Models have the potential to: • Make predictions about the response of a system to change in its drivers • Compare the results of two alternative theories • Describe the effect of complex factors, such as random variation in inputs • Explain how the underlying processes contribute to the result • Extrapolate results to other situations • Predict future events • Translate knowledge and results into a form that can be easily used by non-experts

In short, models are tools for

prediction — interpretation — communication

System dynamics modelling ’Model’ and ’system’ are closely-related concepts. Both: • strive to depict knowledge and ideas in a simple, transparent or efficient way

• strive to describe or explain how something ’works’ • can help predict how something may change in response to a perturbation in external driving forces, i.e. to relate ’cause’ to ’effect’ or ’input’ to ’output’ • many though not all models can be broken down into a number of elements with links between them, i.e. they represent a system

System dynamics modelling • Developed in 1960’s by Jay Forrester initially to describe how interactions between actors and inputs in economic systems govern oscillations like the ’business cycle’ • Describe a system in terms of ’stocks’ (elements), ’flows’ (links) and inputs or drivers • Time-dependent equations govern the dependency of flows on stocks and inputs e.g. Population model for a city

births + population N + deaths

food

+ offspring per adult 

Ecosystem modelling Numerical ecosystem models are generally system dynamics models: • Ecosystem compartments as stocks (state variables) e.g. • C, N, P in biomass and detritus pools • population density of organism groups • water storage in soil, snow pack, canopy • Ecosystem fluxes as flows • into system (e.g. photosynthesis) • out of system (e.g. respiration, evapotranspiration) • between compartments (e.g. litter transfer from vegetation to soil) • External drivers e.g. temperature, solar insolation, rainfall, N deposition • Time-dependent equations describe processes controlling (rates of) fluxes depending on state variables and external drivers

A typical Ecosystem model Drivers - Temperature - Radiation - Precipitation Processes - Light interception - Photosynthesis - Respiration - Hydrology

State variables - Carbon stocks - Leaf area - Soil water - Soil moisture

Fluxes - Exchange of energy and matter between the system and its surrounding - Exchange of energy and matter between the stocks of the system

Keywords from this lecture

• system • element, link, feedback • boundary, driver, state variable • emergent property • structure, function • ecosystem • compartment, flux, process • dynamics

• scientific method, hypothesis falsification • abstraction, conceptual model • scope • functional, mechanistic • deterministic, stochastic • prediction • cause-effect relationship • time-dependent equation • ecosystem model