I'm getting different values when I run ANOVA with the same variables as single and mixed effects

Hi all, I have a vegetation survey data set and I'm trying to determine if there are significant differences in responding diversity variables when comparing between different sites, and different positions on the same site. I also want to see if there are any interactions between site and position that result in significant differences. I've set up my code as such:

summary(aov(nISI~Site*pos, newPDD)) #nISI is a diversity metric calculated at a plot-by-plot level

Now when I run the aov with Site and Position as interacting effects I get this:

> summary(aov(nISI~Site*pos, newPDD))
            Df Sum Sq Mean Sq F value   Pr(>F)    
Site         6  42.51   7.085   7.687 **2.31e-06** ***
pos          1   4.02   4.015   4.357   **0.0405** *  
Site:pos     6  11.65   1.941   2.106   0.0633 .  
Residuals   70  64.51   0.922                     

In this case, both site and position seem to be significant effects when compared independently. However when I compare nISI between positions using a single-factor ANOVA and a t-test:

summary(aov(nISI~pos, newPDD))

The output is:

> summary(aov(nISI~pos, newPDD))
            Df Sum Sq Mean Sq F value Pr(>F)  
pos          1   4.02   4.015   2.775 **0.0996** .
Residuals   82 118.67   1.447                 
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> t.test(nISI~pos)

	Welch Two Sample t-test

data:  nISI by pos
t = 1.6657, df = 81.439, p-value = **0.09962**
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.08501103  0.95954683
sample estimates:
mean in group Ag field   mean in group Stream 
              2.159406               1.722139 

In this case, I get p-values which are different and not significant. Can anyone explain what is going on here? What is the correct value to be using in this case? Why are there different values for the aov output for the same factor?

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