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Benjamin Gibson BIO 332 Data Assignment #2 Spawner-recruit relationships 1. Per capita recruitment decreases at high spawner densities because of density-dependent mortality. This is highly probable due to limited resources. Some likely scenarios include competition among abundant larvae which could lead to starvation, adults eating their own eggs and larvae at high densities, predators aggregating at high egg and larvae densities, increased accumulation of toxic waste and spread of disease at high densities. 2. Density-dependent compensation due to the slope being less than 0. This leads to an almost inevitable population crash and lack of recovery. 3. One valuable generalization is that there is always low recruitment when there are low spawners. Another generalization is that density-dependent mortality is evident, regardless of which model is used.

Data.Assignment2.S-R+Models.BGibson

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Page 1: Data.Assignment2.S-R+Models.BGibson

Benjamin Gibson

BIO 332

Data Assignment #2

Spawner-recruit relationships

1. Per capita recruitment decreases at high spawner densities because of density-dependent

mortality. This is highly probable due to limited resources. Some likely scenarios include

competition among abundant larvae which could lead to starvation, adults eating their

own eggs and larvae at high densities, predators aggregating at high egg and larvae

densities, increased accumulation of toxic waste and spread of disease at high densities.

2. Density-dependent compensation due to the slope being less than 0. This leads to an

almost inevitable population crash and lack of recovery.

3. One valuable generalization is that there is always low recruitment when there are low

spawners. Another generalization is that density-dependent mortality is evident,

regardless of which model is used.

Page 2: Data.Assignment2.S-R+Models.BGibson

Parameters Beverton & Holt Model Ricker Model

a

b

0.584

0.124

0.143

0.009

Table 1. Calculated parameters for both the Beverton & Holt and Ricker

models using 40 years of spawner-recruit data of Atlantic Menhaden.

Page 3: Data.Assignment2.S-R+Models.BGibson

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.84399

R2 0.71232

Adjusted R2 0.70474

Std. Error 0.52948

Observations 40

ANOVA

df SS MS F P-value

Regression 1 26.3781 26.3781 94.0895 7.9061E-12

Residual 38 10.6533 0.28035

Total 39 37.0314

Coefficients Standard

Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.73073237 0.3482151 2.09851 0.042561 0.0258 1.4357 0.02580782 1.43565692 X Variable1 -0.87801647 0.0905174 -9.69997 7.9061E-12 -1.0613 -0.6948 -1.06125940 -0.69477354

Table 2. Using Excel’s Data Analysis add-in, a full statistical output

is provided. Gray cells indicate importance to the diagnostic test.

Page 4: Data.Assignment2.S-R+Models.BGibson

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0

Observed

Predicted Ricker

Predicted B&H

Figure 1. Fitting both the predicted Beverton & Holt (B&H) and

the predicted Ricker model to 40 years of observed spawner-recruit

data of Atlantic Menhaden.

Page 5: Data.Assignment2.S-R+Models.BGibson

y = -0.878x + 0.731R² = 0.712P < 0.0001

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Ln(R

/SSB

)

Ln(SSB)

Figure 2. Natural log of per capita recruitment (R/SSB) as a function of the

natural log of spawning stock biomass (SSB) for Atlantic Menhaden.