**Skill:**

• Testing for association between two species using the chi-squared test with data obtained by quadrat sampling

The presence of two species within a given environment will be dependent upon potential interactions between them

If two species are typically found within the same habitat, they show a **positive** association

- Species that show a positive association include those that exhibit
*predator-prey*or*symbiotic*relationships

If two species tend not to occur within the same habitat, they show a **negative** association

- Species will typically show a negative association if there is
*competition*for the same resources- One species may utilise the resources more efficiently, precluding survival of the other species (
*competitive exclusion*) - Both species may alter their use of the environment to avoid direct competition (
*resource partitioning*)

- One species may utilise the resources more efficiently, precluding survival of the other species (

If two species do not interact, there will be **no** association between them and their distribution will be independent of one another

**Quadrat Sampling**

The presence of two species within a given environment can be determined using quadrat sampling

- A quadrat is a rectangular frame of known dimensions that can be used to establish population densities
- Quadrats are placed inside a defined area in either a random arrangement or according to a design (e.g. belted transect)
- The number of individuals of a given species is either counted or estimated via percentage coverage
- The sampling process is repeated many times in order to gain a representative data set

Quadrat sampling is not an effective method for counting motile organisms – it is used for counting plants and sessile animals

- In each quadrat, the presence or absence of each species is identified
- This allows for the number of quadrats where both species were present to be compared against the total number of quadrats

**Quadrat Sampling Method**

**Chi-Squared Tests**

A chi-squared test can be applied to data generated from quadrat sampling to determine if there is a statistically significant association between the distribution of two species

A chi-squared test can be completed by following five simple steps:

- Identify hypotheses (null versus alternative)
- Construct a table of frequencies (observed versus expected)
- Apply the chi-squared formula
- Determine the degree of freedom (df)
- Identify the p value (should be <0.05)

**Skill:**

• Recognising and interpreting statistical significance

**Example of Chi-Squared Test Application**

The presence or absence of two species of scallop was recorded in fifty quadrats (1m^{2}) on a rocky sea shore

The following distribution pattern was observed:

- 6 quadrats = both species ; 15 quadrats = king scallop only ; 20 quadrats = queen scallop only ; 9 quadrats = neither species

**Step 1:** __Identify hypotheses__

A chi-squared test seeks to distinguish between two distinct possibilities and hence requires two contrasting hypotheses:

*Null hypothesis (*There is**H**):_{0}**no**significant difference between the distribution of two species (i.e. distribution is random)*Alternative hypothesis (*There**H**):_{1}**is**a significant difference between the distribution of species (i.e. species are associated)

**Step 2: ** __Construct a table of frequencies__

A table must be constructed that identifies *expected* distribution frequencies for each species (for comparison against *observed*)

Expected frequencies are calculated according to the following formula:

*Expected frequency = (Row total × Column total) ÷ Grand total*

**Step 3:** __Apply the chi-squared formula__

The formula used to calculate a statistical value for the chi-squared test is as follows:

Where: ∑ = Sum ; O = Observed frequency ; E = Expected frequency

These calculations can be broken down for each part of the distribution pattern to make the final summation easier

Based on these results the statistical value calculated by the chi-squared test is as follows:

- 𝝌
^{2}= (2.20 + 2.38 + 1.59 + 1.73) =**7.90**

**Step 4: ** __Determine the degree of freedom (df)__

In order to determine if the chi-squared value is statistically significant a degree of freedom must first be identified

- The degree of freedom is a mathematical restriction that designates what range of values fall within each significance level

The degree of freedom is calculated from the table of frequencies according to the following formula:

df = (m – 1) (n – 1)

Where: m = number of rows ; n = number of columns

- When the distribution patterns for two species are being compared, the degree of freedom should always be
**1**

**Step 5:** __Identify the p value__

The final step is to apply the value generated to a chi-squared distribution table to determine if results are statistically significant

- A value is considered significant if there is less than a 5% probability (p < 0.05) the results are attributable to chance

When df = 1, a value of greater than 3.841 is required for results to be considered statistically significant (p < 0.05)

- A value of 7.90 lies above a p value of 0.01, meaning there is less than a 1% probability results are caused by chance
- Hence, the difference between observed and expected frequencies
**are**statistically significant

As the results are statistically significant, the null hypothesis is rejected and the alternate hypothesis accepted:

*Alternate hypothesis (*There**H**):_{1}**is**a significant difference between observed and expected frequencies- Because the two species do not tend to be present in the same area, we can infer there is a
*negative*association between them

**Practice Question**