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A scientist is using a population growth model to predict the future of a species in a changing environment. Which of the following factors would be LEAST likely to be included in this model?
\(D\)
→ Population growth models typically focus on factors directly related to population dynamics such as birth rates, death rates, and carrying capacity.
→ While genetic diversity might be included in more complex models, average daily temperature fluctuations are too fine-grained for most population models and would be more relevant in climate models.
\(\Rightarrow D\)
Which of the following best describes the role of predictive models in managing future ecosystems?
\(D\)
→ Predictive models simulate potential future scenarios based on current data and trends.
→ While they don’t provide exact forecasts or guarantee success, they offer valuable insights that guide ecosystem management strategies.
\(\Rightarrow D\)
While models provide valuable insights into potential biodiversity changes, they are not without their shortcomings.
Identify and explain two limitations of models used to predict future impacts on biodiversity. In your answer suggest a way scientists might address or mitigate these limitations. (4 marks)
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Limitation: inadequate data
→ Model accuracy requires adequate data which can be expensive and difficult to obtain.
→ To address this, scientists can use developing technology to capture more data and use machine learning techniques to better interpret that data.
Limitation: inaccurate model inputs (projections)
→ Models often require projections of human behaviour, such as greenhouse gas emissions, which can be highly unpredictable.
→ This uncertainty can significantly affect the accuracy of long-term biodiversity predictions.
→ One way to mitigate this limitation is to use scenario-based modelling, where multiple possible futures are simulated based on different human activity projections.
Limitation: complexity of the system being modelled
→ Ecological interactions are complex systems with unpredictable feedback loops. These systems are extremely difficulty to model.
→ Scientists need to be aware of and incorporate other research into their model. For example, (unexpected) massive methane releases as permafrost melts in the Arctic should be included in any climate model as the science becomes known.
Limitation: inadequate data
→ Model accuracy requires adequate data which can be expensive and difficult to obtain.
→ To address this, scientists can use developing technology to capture more data and use machine learning techniques to better interpret that data.
Limitation: inaccurate model inputs (projections)
→ Models often require projections of human behaviour, such as greenhouse gas emissions, which can be highly unpredictable.
→ This uncertainty can significantly affect the accuracy of long-term biodiversity predictions.
→ One way to mitigate this limitation is to use scenario-based modelling, where multiple possible futures are simulated based on different human activity projections.
Limitation: complexity of the system being modelled
→ Ecological interactions are complex systems with unpredictable feedback loops. These systems are extremely difficulty to model.
→ Scientists need to be aware of and incorporate other research into their model. For example, (unexpected) massive methane releases as permafrost melts in the Arctic should be included in any climate model as the science becomes known.
In an era of rapid environmental change, scientists are developing sophisticated tools to anticipate shifts in biodiversity. Briefly explain two distinct models scientists use to predict future impacts on biodiversity. (2 marks)
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Answers could include two of the following:
→ Population growth models predict changes in species abundance over time, considering factors like birth rates, death rates, and carrying capacity. These models help to forecast potential extinctions or population booms.
→ Global warming models simulate future climate scenarios based on greenhouse gas emissions and temperature changes. This allows scientists to predict shifts in species distributions and habitat suitability.
→ Species loss models estimate the number of species that may go extinct as habitat area decreases. These models are used to predict biodiversity loss due to deforestation or other forms of habitat destruction.
Answers could include two of the following:
→ Population growth models predict changes in species abundance over time, considering factors like birth rates, death rates, and carrying capacity. These models help to forecast potential extinctions or population booms.
→ Global warming models simulate future climate scenarios based on greenhouse gas emissions and temperature changes. This allows scientists to predict shifts in species distributions and habitat suitability.
→ Species loss models estimate the number of species that may go extinct as habitat area decreases. These models are used to predict biodiversity loss due to deforestation or other forms of habitat destruction.