Foundation - 1.2 Systems Flashcards
Systems
An assemblage of constituent parts as well as the interactions and relationships which make up these parts, which together make up an entity
Systems approach
Term used to describe a method of simplifying and understanding a complicated set of interactions
Two ways of studying systems
- reductionist approach
- holistic approach
Reductionist approach
Breaking a system down into its parts and studying each individually
* can be useful for partially understood systems
Holistic approach
Looks at the system’s processes and interactions as a whole
Flows
Provide inputs and outputs of energy and matter
* they can either be transfers or transformations
Transfers
A change in location but no change in matter
Transformations
A change in chemical nature, a change in state or a change in energy as well as a change in location
Storage
Represented by rectangles, usually with their size being proportional to the storage itself
Emergent properties
A property of a system but not of the individual parts of the system
Main types of systems
- Open
- Closed
- Isolated (hypothetical)
Open systems
Both energy and matter are exchanged between the systems and its surrounding
* usually organic (living) systems that interact with surroundings
* e.g. most ecosystems, as well as our bodies
Closed systems
Energy, but no matter, is exchanged between the systems and its surroundings
* usually inorganic
* e.g. the Earth (and its atmosphere) could be considered one, though its debated
Isolated systems
Neither energy nor matter are exchanged between the system and its surroundings
* do not exist naturally, theoretical concept (though universe could be one)
Scales of systems (with examples)
- Small-scale ecological system: bromeliad (plant in rainforest) forms microcosm of life
- Large-scale ecosystem: entire rainforest where countless species interact within a complex web of relationships
- Giant, self-contained system: the Earth’s atmosphere
Gaia hypothesis
Proposed by James Lovelock (1970s)
* Presented Earth as a single, self-regulating system
Tipping point
The minimum amount of change within a system that will destabilise it, causing it to reach a new equilibrium or stable state
Positive feedback loops
The product of a reaction leads to an increase in that reaction, again and again
Regime shift
The formation of a new equilibrium as a result of positive feeback loops
Steps of reaching a tipping point
- The system is subject to some kind of pressure
- This pressure pushes the system towards a tipping point
- The tipping point is reached
- Positive feedback loops accelerate the shift into a new state
- Change is often irreversible or a high cost is required to return it to previous state
Reasons why tipping points are difficult to predict (name 3)
- Often delays of carying length involved in feedback loops which add complexity of modelling systems
- Not all components change abrubtly at the same time
- May be impossible to identify a tipping point until after it has been passed
- One activity in one part of the globe may lead to a system reaching a tipping point elsewhere on the planet
Resilience
Ability of a system to maintain stability and avoid tipping point
Factors affecting stability of system (name 3)
- Disturbance frequency and intensity
- Size of storages
- Species diversity, interactions (competition)
- Trophic complexities (how many system components are there)
- Rate of nutrient or energy influx (how fast nutrients and energy are moving in and out of system)
Model
A simplified version of reality
* can be analysed or tested to learn about how the system works and predict its behaviour
Strengths of models (name 3)
- Models simplify complex systems
- Models allow predictions to be made about how systems will react in response to change
- System inputs can be changed to observe effects and outputs without the need to wait for real-life events to occur
- Easier to understand than the real system
- Results can be shared between researchers and communicated to the public
- Results can warn us about future environmental issues
Limitations of models (name 3)
- Can be oversimplified or inaccurate
- Results depend on the quality of data of inputs
- Results become more uncertain the further they predict into the future
- Different models can show vastly different outputs even if they have the same inputs
- Results can be interpreted differently by different people
- Can be very complex
Static equilibrium
No inputs or outputs (of energy or matter) to the system and therefore the system shows no change over time
* no natural systems are such
Unstable/dynamic equilibrium
Even small disturbances can cause the system to suddently shift
Negative feedback
When the output of a process inhibits or reverses the operation of the same process in such a way as to reduce change
* stabilising as they counteract deviation
Positive feeback
When a disturbance leads to an amplification of that disturbance, destabilising the system and driving it away from its equilibrium
