Marine Science (9693) | Populations and sampling techniques

AS Marine Science (9693) Study Notes: Populations and Sampling Techniques (4.4)

Welcome, marine scientists! This chapter is incredibly important because it moves us from simply identifying organisms (Classification) to actually studying how they live and where they live. To manage and conserve marine ecosystems, we need to know how many organisms there are and what environmental factors control them. This involves using specific ecological definitions and crucial mathematical sampling techniques.

Don't worry if the statistics look daunting—the formulas will be provided in the exam! Your job is to understand when to use them and how to interpret the results. Let's dive in!

Section 1: The Language of Ecology (LO 4.4.1)

We often use everyday words like 'habitat' or 'community', but in Marine Science, these terms have precise scientific definitions. You must know these!

Key Ecological Definitions

• Species: A group of organisms that can reproduce to produce fertile offspring. Example: The Emperor Angelfish (Pomacanthus imperator).


• Population: All the individuals of a single species living in a particular area at the same time. Example: All the individual blue sharks living in the North Atlantic Ocean.


• Community: All the different populations (of different species) living and interacting in a particular area. Example: All the corals, fish, algae, and invertebrates living on a specific section of the Great Barrier Reef.


• Habitat: The natural environment or place where an organism usually lives. Example: The rocky subtidal zone, or the deep abyssal plain.


• Ecosystem: The whole functioning unit, including both the community (all living organisms) and the physical environment (abiotic factors) they interact with. Example: A mangrove forest ecosystem.


• Niche: The exact role an organism plays in its community. This includes everything it eats, where it lives, its temperature tolerance, and how it interacts with other species. Think of it as the organism’s ‘job’ or ‘way of life’.

Quick Tip: Hierarchy of complexity goes from Species $\rightarrow$ Population $\rightarrow$ Community $\rightarrow$ Ecosystem. (Silly Penguins Can't Eat helps remember the order!)

Section 2: Factors Influencing Distribution and Abundance (LO 4.4.2)

The distribution (where an organism is found) and abundance (how many organisms there are) are controlled by surrounding factors, which we classify as either living (biotic) or non-living (abiotic).

2.1 Biotic Factors (Living Interactions)

These involve interactions between organisms. The syllabus requires knowledge of:

• Competition: Demand by two or more organisms for a resource that is limited supply.
  • Intra-specific competition: Competition within the same species (e.g., two male seals fighting over territory).
  • Inter-specific competition: Competition between different species (e.g., corals and algae competing for light and space).


• Predation: One organism (predator) consumes another (prey). This heavily influences population size (e.g., sharks hunting fish).


• Symbioses: Close interactions between two different species (covered in detail in Topic 3.1):
  • Mutualism (both benefit, e.g., coral polyps and zooxanthellae).
  • Commensalism (one benefits, the other is unaffected).
  • Parasitism (one benefits, the host is harmed).


• Disease: Can cause high mortality and severely limit the abundance of a population.

2.2 Abiotic Factors (Non-Living Environment)

These are the physical or chemical components of the environment:

• Salinity (salt concentration)
• Temperature
• pH (acidity/alkalinity)
• Oxygen concentration and Carbon Dioxide concentration
• Light availability (crucial for photosynthesis in surface waters)
• Turbidity (how cloudy the water is, affecting light penetration)
• Wave/Tide action (physical stress, especially in the littoral zone)
• Nutrient availability (e.g., nitrates, phosphates)
• Exposure to air (critical for organisms living on tidal shores).

Applying Factors to a Named Marine Ecosystem

To demonstrate understanding, you must be able to apply these factors to a real example.

Example: Factors affecting a named organism like the limpet (Patella vulgata) on a rocky shore:

• Abiotic Factor (limiting high up the shore): Exposure to air, leading to desiccation (drying out). Limpets must clamp down tightly to prevent water loss.
• Abiotic Factor (limiting low down the shore): Wave action might be too severe, but often less limiting than exposure.
• Biotic Factor: Inter-specific competition with barnacles and mussels for limited attachment space.
• Biotic Factor: Predation pressure from crabs and seabirds, especially when exposed during low tide.

Section 3: Estimating Population Size: Mark-Release-Recapture (LO 4.4.3 & 4.4.4)

In the ocean, it is impossible to count every fish, crab, or shark. Instead, we use estimation methods. The syllabus requires you to understand the Mark-Release-Recapture (MRR) method, often analyzed using the Lincoln Index.

The Mark-Release-Recapture Method

This technique is used for mobile organisms (those that move around, unlike barnacles fixed to a rock).

Step-by-step Process:

1. Capture and Mark (\(n_1\)): Capture a sample of organisms from the population. Count them (\(n_1\)). Mark them safely (e.g., tagging fish, painting a shell) and record the markings.
2. Release: Return the marked individuals to their habitat and allow them time to mix randomly back into the total population.
3. Recapture (\(n_2\) and \(m_2\)): Take a second sample later. Count the total number of individuals caught in this second sample (\(n_2\)). Count how many of these individuals are marked (\(m_2\)).
4. Estimate: Use the Lincoln Index formula to estimate the total population size (N).

The Lincoln Index Formula (LO 4.4.4)

This formula assumes the ratio of marked to unmarked individuals in the second sample is the same as the ratio of marked individuals to the entire population (N).

$$N = \frac{n_1 \times n_2}{m_2}$$

Where:

• \(N\) = Estimate of the total population size.
• \(n_1\) = Number of individuals captured in the first sample (and marked).
• \(n_2\) = Total number of individuals (both marked and unmarked) captured in the second sample.
• \(m_2\) = Number of marked individuals recaptured in the second sample.

Limitations of the Lincoln Index (LO 4.4.4)

The reliability of the Lincoln Index depends on several crucial assumptions being met. If these are violated, the population estimate (N) will be inaccurate.

Common limitations and why they occur:

1. Assumption: No births, deaths, immigration, or emigration occurred between sampling.
   Limitation: This is rarely true in marine environments. If many individuals left (emigration), N will be overestimated.
2. Assumption: The marked individuals mixed randomly back into the population.
   Limitation: If individuals stayed grouped together, they might be over-represented in the second sample, leading to an underestimate of N.
3. Assumption: Marking does not affect survival or behaviour.
   Limitation: A highly visible mark might increase predation risk. If marked animals die quickly, \(m_2\) will be low, leading to an overestimate of N.
4. Assumption: The mark remains visible and does not wear off.
   Limitation: If the mark disappears, \(m_2\) will be low, leading to an overestimate of N.
5. Assumption: Individuals are equally likely to be caught in both samples (no "trap-happy" or "trap-shy" behaviour).
   Limitation: If an organism learns to avoid the trap (trap-shy), \(m_2\) will be low, resulting in an overestimate of N.

Section 4: Investigating Distribution and Abundance (LO 4.4.5 & 4.4.6)

For organisms that are sessile (attached) or slow-moving, different techniques are used to study their abundance and how they are distributed, especially when investigating environmental gradients, such as on a rocky shore (littoral zone).

4.1 Random vs. Systematic Sampling (LO 4.4.5)

We choose a sampling strategy based on the ecosystem and the question we are asking.

1. Random Sampling
   • When to use: When the study area is relatively uniform (homogeneous) and you want to avoid bias.
   • Method: Use random number generators or coordinates to decide where to place the sampling equipment (like a quadrat).
   • Advantage: Ensures that every part of the habitat has an equal chance of being sampled, giving a statistically unbiased estimate of abundance.
   • Disadvantage: May miss rare species or fail to capture patterns linked to environmental changes (gradients).


2. Systematic Sampling
   • When to use: When the study area shows a clear environmental change or gradient (like altitude, depth, or distance from the water line). This is essential in the littoral zone.
   • Method: Samples are taken at fixed intervals along a line (a transect).
   • Advantage: Directly shows how distribution and abundance correlate with the changing environmental factor along the line.
   • Disadvantage: It is biased, as only areas along the line are sampled, potentially missing diversity elsewhere.

4.2 Sampling Equipment (LO 4.4.6)

The syllabus requires familiarity with different transects and quadrats used in the littoral zone:

• Frame Quadrats: These are square frames of a defined area (e.g., $0.25\text{ m}^2$) used for estimating abundance or percentage cover of sessile organisms (like barnacles or algae).


• Line Transect: A rope or tape measure is laid out across the study area (e.g., from high tide to low tide). Only organisms touching the line are recorded.
  • Use: Provides a simple measure of distribution (presence/absence) along a gradient.


• Belt Transect: This combines the line transect with quadrats. A tape measure is laid out, and quadrats are placed at regular intervals along the line (or placed adjacent to the line).
  • Use: Provides more detailed information on both distribution and abundance along the environmental gradient. This is generally the most informative method for studying zonation on shores.

Section 5: Analyzing Relationships and Diversity (LO 4.4.7 & 4.4.8)

Once data is collected (e.g., organism abundance and corresponding abiotic factor measurements), we need statistical tools to analyze patterns and draw conclusions.

5.1 Measuring Species Diversity: Simpson's Index (D) (LO 4.4.7)

Species diversity measures two things: Species richness (the number of different species present) and Relative abundance (how many individuals of each species there are).

Simpson's Index of Diversity (\(D\)) gives us a single number representing the biodiversity of a habitat. High values of D indicate a diverse, stable ecosystem (like a coral reef). Low values indicate lower diversity (like a sandy shore).

The Simpson's Index Formula:

$$D = 1 - \left( \sum \left( \frac{n}{N} \right)^2 \right)$$

Where:

• \(\sum\) = Sum of (total)
• \(n\) = Number of individuals of each different species
• \(N\) = The total number of individuals of all the species

Interpreting D:

The result of D will be a value between 0 and 1.

• If D is close to 1: High species diversity. The habitat is stable and not dominated by a few species.
• If D is close to 0: Low species diversity. The habitat is unstable or dominated by just one or two species.

5.2 Testing for Correlation: Spearman's Rank (LO 4.4.8)

When you have sampled organism abundance alongside an environmental factor (e.g., counting limpets at various distances from the high tide mark), you use Spearman’s Rank Correlation (\(r_s\)) to determine if there is a relationship between the two variables.

$$r_s = 1 - \left( \frac{6 \times \sum D^2}{n^3 - n} \right)$$

Where:

• \(\sum\) = Sum of (total)
• \(n\) = Number of pairs of items in the sample (number of points/measurements).
• \(D\) = Difference in rank between each pair of measurements (e.g., Rank of Abundance minus Rank of Temperature).

Interpreting the Spearman's Rank Value (\(r_s\)):

The result of \(r_s\) is a value between -1 and +1.

• \(r_s = +1\): Perfect positive correlation. As one factor increases (e.g., temperature), the abundance of the species always increases.
• \(r_s = -1\): Perfect negative correlation. As one factor increases (e.g., distance up the shore), the abundance of the species always decreases.
• \(r_s = 0\): No correlation. The two variables show no obvious relationship.

Did you know? You often compare the calculated \(r_s\) value to a critical values table to see if the correlation is statistically significant (meaning the result is unlikely to be due to chance).