Brent Cornell

    
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)

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)

    
Example of Chi-Squared Test Application

The presence or absence of two species of scallop was recorded in fifty quadrats (1m²) 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 (H₀): There is no significant difference between the distribution of two species (i.e. distribution is random)
• Alternative hypothesis (H₁): There 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:

• 𝝌²  =  (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 (H₁): There 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

Two species of fir tree are found along the coast of Southern California.
These two tree species are the Grand Fir (Abies grandis) and the Noble Fir (Abies procera).
Their distribution patterns were establsihed via 150 quadrat samples, yielding the following results:
25 = both present ; 30 = Noble Fir only ; 45 = Grand Fir only ; 50 neither present
Activity: Use the chi-squared test to determine if these two plant species show association.