Practical skills Revision Notes

Investigating Distribution and Abundance

Ecological sampling methods are used to estimate the distribution (where organisms are found) and abundance (how many organisms are present) of species in an area. It is rarely possible to count every individual, so sampling provides a manageable way to estimate population characteristics. Random sampling involves selecting sample sites without bias, for example, by using random number generators for coordinates to place quadrats. This ensures the sample is representative of the whole habitat. Systematic sampling, such as using a transect, is used when a clear environmental gradient is being investigated.

Using Quadrats for Abundance

A quadrat is a square frame of a known area, often 0.25 m² or 1 m², used for sampling plants or slow-moving animals. To use quadrats effectively for abundance: 1. Choose a suitable size quadrat. 2. Place it randomly within the study area (e.g., using random numbers for coordinates). 3. Count the number of individuals of the target species within the quadrat. For plants, percentage cover might also be estimated. 4. Take multiple samples (at least 10-20) to get a reliable average. 5. Calculate the mean number of organisms per quadrat, or the mean percentage cover. From this, the total population size or density can be estimated for the entire study area by scaling up the average.

Using Transects for Distribution along a Gradient

Transects are lines used to investigate how the distribution and abundance of organisms change across an environmental gradient (e.g., from dry to wet, or shaded to exposed). There are two main types:

• Line Transect: A line is laid out, and any organisms touching the line at set intervals are recorded. This provides basic presence/absence data.
• Belt Transect: A line is laid out, and quadrats are placed at regular intervals along the line. This allows for detailed abundance data (counts or percentage cover) to be recorded within each quadrat, alongside measurements of abiotic or biotic factors, showing how they correlate with species distribution.

Measuring Biotic and Abiotic Factors

When sampling, it is important to measure relevant biotic (living) and abiotic (non-living) factors alongside organism data. Biotic factors include the presence or absence of other species, signs of competition, or herbivory. Abiotic factors include light intensity (using a light meter), temperature (using a thermometer), soil moisture (using a moisture meter), soil pH (using a pH meter or indicator solution), and wind speed (anemometer). Recording these factors allows for analysis of how environmental conditions influence the distribution and abundance of the organisms being studied. For example, a species might be more abundant in areas with higher soil moisture or lower light intensity.

Measuring Pulse Rate

Pulse rate is a measure of how many times your heart beats per minute. To measure pulse rate accurately, locate a pulse point such as the radial artery in the wrist or the carotid artery in the neck. Use two fingers (not your thumb) and count the number of beats over 15 seconds. Multiply this number by four to get the beats per minute. It's important to take several readings and calculate an average for greater reliability. Ensure the participant is at rest for their baseline reading, and note the time intervals between measurements after exercise, for example, every minute for 5-10 minutes.

Measuring Ventilation Rate

Ventilation rate is the number of breaths taken per minute. To measure this, count the number of times a person's chest rises and falls over 30 seconds. Multiply this by two to calculate breaths per minute. Ensure measurement is done discreetly to avoid the person consciously altering their breathing pattern. Like pulse rate, a baseline reading before exercise is crucial. After exercise, continue to measure ventilation rate at regular intervals to observe the recovery pattern.

Experimental Design and Variables

When investigating physiological responses to exercise, the experimental setup must be carefully controlled. The independent variable is the level of exercise (e.g., before, during, after). The dependent variables are the physiological responses being measured: pulse rate and ventilation rate. Control variables are factors that must be kept constant to ensure a fair test. These include the type of exercise (e.g., step-ups), the intensity of exercise (e.g., height of step, pace), the duration of exercise, and environmental conditions like room temperature. This allows for valid conclusions about the effect of exercise. Repeating the experiment with multiple individuals or multiple trials for one individual enhances the reliability of the results.

Safety and Data Analysis

Safety is paramount. Ensure all participants are medically fit for exercise and instruct them to stop if they experience discomfort or dizziness. Have water available and ensure a clear space for exercise. The data collected (pulse and ventilation rates over time) should be recorded in a clear table. Typically, a line graph is used to plot the results, with time on the x-axis and the physiological response (pulse or ventilation rate) on the y-axis. This allows for easy visualisation of trends: rates increasing during exercise and gradually returning to baseline during recovery. Analysis should include comparing baseline rates to post-exercise rates and commenting on the recovery time.

Using a Light Microscope Safely and Effectively

To use a light microscope safely, always carry it with two hands, one holding the arm and the other supporting the base. Place it on a stable surface. When setting up, plug it in and switch on the light source. Start with the lowest power objective lens and turn the coarse focus knob to bring the stage up close to the objective, then slowly move it down until the image is roughly in focus. Use the fine focus knob for precise focusing. Adjust the diaphragm or aperture to control the brightness and contrast of the image. Always ensure the highest power objective lens does not touch the slide. Remove slides from the stage before returning the objective to the lowest power.

Calculating Magnification

Magnification is a measure of how much larger an image appears compared to the actual size of the object. For a light microscope, total magnification is calculated by multiplying the magnification of the eyepiece lens by the magnification of the objective lens. For example, if an eyepiece lens has a magnification of 10x and an objective lens has a magnification of 40x, the total magnification is 10 x 40 = 400x. This means the image you see is 400 times larger than the actual specimen. The relationship between image size, actual size, and magnification is often remembered using the formula: Image size = Actual size × Magnification.

Producing Labelled Scientific Drawings

Scientific drawings are important for recording observations accurately. They should be large, pencil-drawn, and fill a significant portion of the available space. Use clear, continuous lines, avoiding shading or sketching. All observable details should be included. Labels should be clear, written horizontally, and connected to the specific feature with uncrossed, straight lines drawn with a ruler. Include a title for the drawing, the total magnification used, and a scale bar if measurements are available. The drawing reflects what is actually seen, not what you expect to see.

Measuring Specimens and Converting Units

To measure the actual size of specimens under a microscope, a calibrated eyepiece graticule is used. The eyepiece graticule is a small ruler placed in the eyepiece, and its divisions must be calibrated against a stage micrometer (a slide with a known scale). Once calibrated, you can measure the image size of a feature using the eyepiece graticule. The actual size is then calculated by dividing the image size by the total magnification, or by using the calibrated value of each graticule division. Common unit conversions for microscope measurements include 1 mm = 1000 µm (micrometres) and 1 µm = 1000 nm (nanometres).

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Practical planning must identify the independent variable that is changed, the dependent variable that is measured and important control variables kept constant. Follow a repeatable method and record units. Complete a risk assessment: identify each hazard, explain the risk and use a suitable safety control. Repeat measurements, calculate a mean, plot an appropriate graph and evaluate anomalies, uncertainty and limitations before suggesting justified improvements.

Experimental Setup for Investigating Photosynthesis

A common method uses pondweed (e.g., Elodea) submerged in water within a test tube or beaker. A light source (lamp) is positioned at varying distances to change light intensity. Carbon dioxide can be supplied by adding sodium hydrogen carbonate to the water. A thermometer is used to monitor temperature. Oxygen bubbles produced by the pondweed during photosynthesis can be counted, or collected in an inverted measuring cylinder or gas syringe to quantify the gas volume. Safety precautions include keeping electrical lamps away from water and handling living organisms carefully.

Variables and Controls

When investigating a factor like light intensity: the independent variable is the light intensity (adjusted by moving the lamp). The dependent variable is the rate of photosynthesis, measured by the number of oxygen bubbles produced per minute or the volume of oxygen collected. Control variables to keep constant include: carbon dioxide concentration (e.g., by adding a fixed amount of sodium hydrogen carbonate solution), temperature (e.g., by using a water bath or ensuring consistent room temperature), and the type and amount of pondweed used (e.g., same length of stem, same species). Maintaining these controls ensures a fair test.

Measurement and Data Analysis

To measure the rate of photosynthesis, count the number of oxygen bubbles produced over a set time (e.g., 5 minutes). Repeat each measurement at least three times for reliability and calculate an average. Plotting a graph with the independent variable (e.g., light intensity) on the x-axis and the rate of photosynthesis (e.g., bubbles per minute) on the y-axis helps visualise the relationship. The graph typically shows an initial increase in photosynthesis rate as the limiting factor increases, followed by a plateau when another factor becomes limiting.

Evaluating the Experiment and Improving Accuracy

Evaluation involves identifying sources of error. Bubble counting can be inaccurate due to varying bubble sizes. Collecting gas in a measuring cylinder provides a more accurate volume. Temperature fluctuations can be a problem; a water bath can regulate temperature more effectively. Ensuring the pondweed is healthy and acclimatised before starting the experiment is also important. Percentage error can be calculated for measurements to consider the accuracy of equipment (e.g., measuring cylinder volume). Repeating trials and averaging results reduces the impact of random errors.