AQA Environmental Science Paper 2: Short Answer Topic Notes

Simpson's Diversity Index

Purpose and Application Simpson's Diversity Index is used to test, calculate, and interpret species diversity based on sample data. It provides a more comprehensive measure than species richness alone because it accounts for both the number of species present and the relative abundance (evenness) of each species.

The Mathematical Formula

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

• $n$ = the number of individuals belonging to each individual species.
• $N$ = the total number of all individuals of all species combined.
• $D$ = the diversity index, ranging from a value of $0$ to $1$.

Interpreting the Index (D)

• Values close to 1: Indicate high diversity. This suggests a community with many different species that are evenly distributed. Such communities are generally more stable.
• Values close to 0: Indicate low diversity. This suggests a community dominated by one or two species.

Step-by-Step Calculation Method

1. For every individual species in the sample, calculate $n(n-1)$.
2. Sum all the results from step 1 to find $\sum n(n-1)$.
3. Calculate the total number of individuals ($N$) and determine $N(N-1)$.
4. Divide the result of step 2 by the result of step 3 ($\frac{\sum n(n-1)}{N(N-1)}$).
5. Subtract the final fraction from $1$ to find $D$.

Worked Example

Calculation: $D = 1 - \left( \frac{158}{20 \times 19} \right)$$D = 1 - \left( \frac{158}{380} \right)$$D = 1 - 0.416$$D = 0.584$

Interpretation Language for Exams

• "A higher $D$ value indicates greater species diversity."
• "A $D$ value of X suggests the community is [relatively diverse / dominated by a few species]."
• "Comparing two sites: site A ($D = 0.8$) has greater diversity than site B ($D = 0.3$)."

Common Exam Errors

• Forgetting the final step of subtracting the fraction from $1$.
• Using the square of $n$ ($n^2$) instead of the prescribed $n(n-1)$.
• Assigning units to the result; $D$ is dimensionless (has no units).
• Confusing species richness (the simple count of species) with diversity (which combines richness and evenness).

Spearman's Rank Correlation

Purpose and Application This statistical test assesses whether there is a correlation between two variables and determines the strength and direction of that relationship.

The Mathematical Formula

$r_s = 1 - \left[ \frac{6 \sum d^2}{n(n^2-1)} \right]$

• $d$ = the difference between the ranks assigned to each pair of data.
• $n$ = the number of data pairs.
• $r_s$ = Spearman's rank coefficient, ranging from $-1$ to $+1$.

Interpreting the Coefficient (r_s)

• +1: Perfect positive correlation (as one variable increases, the other increases).
• -1: Perfect negative correlation (as one variable increases, the other decreases).
• 0: No correlation exists.
• Distance from zero: The closer the value is to $\pm 1$, the stronger the relationship.

Step-by-Step Method

1. Rank both variables separately. You can rank from highest to lowest or vice versa, but you must be consistent for both variables.
2. For each pair of data, calculate the difference ($d$) between the two ranks ($rank_1 - rank_2$).
3. Square each difference to get $d^2$.
4. Sum all the squared differences to find $\sum d^2$.
5. Substitute the values into the formula.
6. Compare the calculated $r_s$ to a critical value table using the correct $n$ and significance level (typically $p = 0.05$).

The Critical Value Rule

• If the absolute value of $r_s$ ($|r_s|$) is higher than or equal to ($\geq$) the critical value, the correlation is significant. You reject the null hypothesis.
• If $|r_s|$ is lower than ($<$) the critical value, the correlation is not significant. You accept the null hypothesis.

Exam Writing Guide When discussing results, always include:

• The calculated $r_s$ value and the sample size $n$.
• The critical value and the significance level used.
• A statement on significance.
• A description of the direction and strength. Example: "There is a significant positive correlation between X and Y ($r_s = 0.82$, $n = 10$, $p < 0.05$), suggesting that as X increases, Y increases."

Common Exam Errors

• Ranking in the wrong direction or failing to handle ties correctly (for tied data, average the ranks; e.g., if two items are joint 3rd and 4th, both receive rank $3.5$).
• Stating the coefficient without comparing it to a critical value.
• Assuming correlation implies causation.

Lincoln Index (Mark-Release-Recapture)

Purpose and Application This method is used to estimate the total population size of a species (usually mobile animals) using capture data.

The Mathematical Formula

$N = \frac{M \times C}{R}$

• $N$ = the estimated total population size.
• $M$ = the number of individuals caught, marked, and released in the first sample.
• $C$ = the total number of individuals caught in the second sample.
• $R$ = the number of marked individuals recaptured in the second sample.

Worked Example

• First catch ($M$): $40$ woodlice are marked and released.
• Second catch ($C$): $35$ individuals are caught.
• Recaptures ($R$): Of the $35$, $7$ were found to be marked.
• Calculation: $N = \frac{40 \times 35}{7} = \frac{1400}{7} = 200$.

Required Assumptions To be valid, these five assumptions must be met:

1. Marked individuals must mix randomly and completely with the rest of the population.
2. The population must be closed; there are no births, deaths, immigration, or emigration between the two sampling events.
3. The marking process does not affect the animal's survival (e.g., making them more visible to predators) or behavior.
4. Marks are not lost or rubbed off between samples.
5. Both sampling events are random.

Impact of Violated Assumptions

• Deaths or Emigration: If marked animals die or leave, $R$ will be lower, leading to an overestimation of $N$.
• Immigration: New unmarked animals entering the population increase the total population but decrease the proportion of marked animals caught ($R$ is lower), leading to an overestimation of $N$.
• Mark Loss: If marks are lost, $R$ is lower, causing an overestimation of $N$.
• Non-random mixing: If marked animals become "trap-shy" and avoid recapture, $R$ is lower, leading to an overestimation of population size.

Marine Productivity & The Photic Zone

Definitions

• Photic Zone: The surface layer of the ocean where light penetration is sufficient for photosynthesis. Usually ranges from $0\text{--}200\,m$, though depth varies based on water clarity.
• Aphotic Zone: The region below the photic zone where light is insufficient for photosynthesis; organisms here rely on organic matter sinking from above.
• Compensation Depth: The specific depth where the rate of photosynthesis exactly equals the rate of respiration ($\text{Net Production} = 0$).
• Primary Productivity: The rate at which organic matter is produced by photosynthesis per unit area per unit time.

Factors Limiting Productivity

• Light: Intensity decreases with depth. Turbidity (closeness/cloudiness of water) reduces light penetration further.
• Nutrients: Nitrogen (N) and Phosphorus (P) are often the primary limiting factors in the open ocean. Upwelling is required to bring these from the deep to the surface.
• Temperature: Warmer waters increase enzyme-driven reaction rates. However, a strong thermocline (temperature gradient) can prevent nutrient-rich deep water from mixing with the surface.
• CO2: Rarely a limiting factor in marine environments.

Seasonal Productivity Patterns (Temperate Seas)

• Spring Bloom: Increasing light levels and high nutrient levels (mixed up during winter) lead to rapid phytoplankton growth.
• Summer: Productivity drops. Nutrients are depleted by the spring bloom, and a thermocline forms, preventing further upwelling of nutrients.
• Autumn Mini-bloom: The thermocline breaks down as water cools, allowing a brief period of nutrient mixing before light levels drop too low.
• Winter: Productivity is at its lowest because light is the limiting factor, despite nutrients being abundant.

Upwelling and Efficiency

• Upwelling: Wind-driven divergence of surface water causes cold, nutrient-rich deep water to rise. This creates highly productive zones (e.g., the Peruvian coast) which are the basis for major commercial fisheries.
• Food Chain Efficiency: Only approximately $10\%$ of energy transfers between trophic levels. Short food chains (e.g., phytoplankton $\rightarrow$ fish) are more efficient than long ones.

r vs K Selection Strategies

Core Concept Species evolve different reproductive strategies depending on the stability and characteristics of their environment.

Comparison Table

Survivorship Curves

• Type I (K-selected): High survival rate in early and middle life, followed by a rapid decline in old age (e.g., humans).
• Type II: A constant mortality rate experienced throughout the entire lifespan (e.g., many birds).
• Type III (r-selected): Extremely high mortality rates for the young; the few survivors live for a long time (e.g., most plants, many fish).

Conservation Implications K-selected species are considerably more vulnerable to population decline because they are slow to recover from exploitation or habitat loss due to their low reproductive rates. Conversely, r-selected species can recover rapidly after a disturbance.

Monitoring Technologies

eDNA (Environmental DNA)

• Process: Organisms shed DNA into water, soil, or air. Samples are collected and filtered. PCR (Polymerase Chain Reaction) amplifies specific sequences, which are then compared to reference databases.
• Applications: Detecting rare or cryptic species (e.g., great crested newts), monitoring invasive species (e.g., signal crayfish), and conducting broad biodiversity surveys (metabarcoding).
• Advantages: Non-invasive; highly sensitive (detects low densities); cost-effective.
• Limitations: DNA degrades within hours/days; cannot determine exact population size or if the organism is currently alive; risk of cross-contamination.

Satellite Tracking

• Process: Transmitters (GPS/ARGOS) are attached to animals, or satellites use remote sensing to monitor habitats.
• Applications: Mapping migration routes (whales, birds); monitoring deforestation rates (using NDVI - Normalised Difference Vegetation Index); tracking sea surface temperatures and ice cover.
• Advantages: Covers vast, inaccessible areas; provides real-time, continuous data; long-term data collection.
• Limitations: High equipment and operation costs; battery life limitations; tags may influence animal behavior or survival.

Habitat Factors: Area, Shape & Corridors

Island Biogeography Theory

• Proposed by MacArthur and Wilson. Larger habitats support more species because they have lower extinction rates and more diverse microhabitats.
• Species-Area Relationship: $S = cA^z$ (Species number increases with area).

Habitat Shape and Edge Effects

• Edge Effect: The boundary between a habitat and the surrounding land has different abiotic conditions (higher light, more wind). Edge species thrive here, but interior specialists (who require stable, deep-habitat conditions) suffer.
• Circular Reserves: Have a low edge-to-interior ratio, which is ideal for protecting interior specialists.
• Elongated/Fragmeted Reserves: Have a high edge-to-interior ratio, making them susceptible to edge effects.

Habitat Corridors These are linear strips (e.g., hedgerows, wildlife bridges) that connect isolated habitat patches.

• Benefits: Enable gene flow (reducing inbreeding), allow recolonization of patches after local extinction, and facilitate range shifts due to climate change.
• Risks: May facilitate the spread of diseases or invasive species.

SLOSS Debate

• Single Large: Better for interior specialists and large populations; fewer edge effects.
• Several Small: Provides redundancy (if one patch is hit by disease, others survive); geographically spreads risk.
• Current Consensus: The ideal setup is several connected reserves, which is better than one single large reserve, which is in turn better than several isolated small patches.