# Practical: Data loggers & Computer Modelling ## Data Loggers - **Data loggers** are tools that enable rapid and efficient collection of data - Can be used for straightforward measurements like pulse rate or for more complex setups - Simple applications: recording breathing rate over time - Complex applications: multiple probes connected to a central computer, each monitoring different variables simultaneously - Once data is captured by a data logger, it can be transferred to a computer and formatted into tables - Computers can then process this data to: - Calculate means and other statistical measures - Perform statistical tests to identify significance - Generate graphs and visual representations automatically

Data loggers eliminate human error in recording and allow continuous monitoring over extended periods that would be impractical manually

## Uses of Computer Modelling - Computer modelling enables the study of **theoretical impacts** on populations from various biological processes: - **Ecological succession and zonation**: tracking how communities change over time in a habitat - **Infectious disease transmission and epidemiology**: predicting how diseases spread through populations - **Predator-prey relationships**: examining population cycles and interactions between species - **Natural selection**: simulating evolutionary changes driven by environmental pressures - **Genetic drift**: modelling random changes in allele frequencies in small populations

A major advantage of computer modelling is the ability to **accelerate time**, allowing predictions of future population and environmental outcomes

## Computer Programs and Natural Selection - Computer programs can **simulate the effects of natural selection** on populations - Typical starting conditions: - Begin with a simple **simulated population** - No particular selection pressures acting initially - Creates a baseline for comparison ### Investigating Natural Selection Through Simulation Natural selection is investigated by **modifying various factors** and observing their effects on the simulated population. **Examples of factors that can be manipulated:** 1. **Biotic and abiotic selection pressures** - Presence or absence of predators - Disease introduction or removal - Food availability changes 2. **Mutation parameters** - Introducing new, specifically chosen mutations into the population - Adjusting the probability of new mutations appearing 3. **Genetic dominance** - Switching which alleles are dominant versus recessive - Observing how inheritance patterns affect population outcomes 4. **Environmental conditions** - Changing the habitat the species occupies - Altering temperature, moisture, or other physical factors 5. **Species characteristics** - Modifying one or more adaptations of the species - Testing how different traits affect survival

**Selection pressure**: an environmental factor that affects an organism's ability to survive and reproduce

### Analysing Simulation Results - The program models these changing factors and calculates **probabilities of different outcomes** for the simulated population - Results show how populations might evolve under various conditions - **Time acceleration** is particularly valuable here: - Natural selection often occurs over extended time periods - Can span many generations for most species - Simulations compress this into experimentally manageable timescales - Allows observation of evolutionary effects that would be impossible to witness in real-time ## Computer Programs and Genetic Drift ### Understanding Genetic Drift - In **significantly small populations**, chance events can influence which alleles are passed to the next generation - Over time, some alleles may be lost entirely or become more common purely by **random chance** - When gradual changes in allele frequencies occur in a small population due to chance (not natural selection), this is called **genetic drift**

**Genetic drift**: random changes in allele frequencies in a population, particularly significant in small populations

### Modelling Genetic Drift - Computer programs similar to those used for natural selection can model genetic drift - Key differences in the simulation: - Focus on **small population sizes** where random events have larger impacts - Changes occur **without selection pressures** - purely by chance - Tracks how allele frequencies fluctuate randomly over generations - Unlike natural selection models, genetic drift simulations demonstrate: - How beneficial alleles can be lost by chance - How neutral or even slightly deleterious alleles can increase in frequency - The importance of population size in maintaining genetic diversity ### Applications Computer modelling of genetic drift helps understand: - Conservation biology: risks facing endangered species with small populations - Population genetics: how genetic diversity is maintained or lost - Evolution: the relative importance of selection versus chance in different scenarios

Small populations are particularly vulnerable to genetic drift, which can reduce genetic diversity and potentially harm the population's long-term survival

## Advantages of Computer-Based Practical Work - **Speed**: Processes that take years or decades can be observed in minutes or hours - **Control**: Variables can be precisely manipulated in ways impossible in real experiments - **Repeatability**: Identical starting conditions can be recreated perfectly - **Ethical**: No harm to living organisms - **Cost-effective**: No need for maintaining live specimens or expensive field work - **Multiple scenarios**: Easy to test many different conditions rapidly