[1] "gamma_diversity"
richness_proportion ~ proportion_impervious + proportion_developed_low + 
    proportion_cultivated_crops

Call:
glm(formula = gamma_div_step$formula, data = meta)

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)   
(Intercept)                    0.4900     0.1352   3.625  0.00169 **
proportion_impervious        -21.4916     9.8307  -2.186  0.04086 * 
proportion_developed_low      57.0673    26.9195   2.120  0.04671 * 
proportion_cultivated_crops -128.2876    50.9616  -2.517  0.02046 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.002488317)

    Null deviance: 0.145083  on 23  degrees of freedom
Residual deviance: 0.049766  on 20  degrees of freedom
AIC: -70.174

Number of Fisher Scoring iterations: 2

[1] "mutualist_mod"
proportion_mutualist ~ proportion_developed + proportion_impervious

Call:
glm(formula = mutualist_step$formula, data = meta)

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)            5.193e-01  1.163e-01   4.464 0.000214 ***
proportion_developed  -3.020e+07  1.241e+07  -2.433 0.024022 *  
proportion_impervious  3.020e+07  1.241e+07   2.433 0.024022 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.09029335)

    Null deviance: 2.4816  on 23  degrees of freedom
Residual deviance: 1.8962  on 21  degrees of freedom
AIC: 15.192

Number of Fisher Scoring iterations: 2

[1] "pathogen_mod"
proportion_pathogen ~ proportion_developed + proportion_developed_high

Call:
glm(formula = pathogen_step$formula, data = meta)

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 1.4237     0.2116   6.728 1.17e-06 ***
proportion_developed      -50.0094    12.6329  -3.959 0.000717 ***
proportion_developed_high 470.0978   119.2377   3.943 0.000745 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.06587141)

    Null deviance: 2.4196  on 23  degrees of freedom
Residual deviance: 1.3833  on 21  degrees of freedom
AIC: 7.6231

Number of Fisher Scoring iterations: 2

[1] "permanova_mod"
Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999

vegan::adonis2(formula = otu_table(ps_meta) ~ meta$proportion_developed + meta$proportion_developed_low + meta$proportion_cultivated_crops, data = meta)
                                 Df SumOfSqs      R2      F Pr(>F)   
meta$proportion_developed         1   0.7262 0.07285 1.8169  0.006 **
meta$proportion_developed_low     1   0.5731 0.05749 1.4337  0.062 . 
meta$proportion_cultivated_crops  1   0.6756 0.06777 1.6903  0.017 * 
Residual                         20   7.9939 0.80189                 
Total                            23   9.9688 1.00000                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[1] "raretaxa_mod"
proportion_rare_taxa ~ proportion_developed + proportion_impervious + 
    proportion_developed_high + proportion_developed_med + proportion_developed_low

Call:
glm(formula = raretaxa_step$formula, data = meta)

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.240e+00  8.701e-01   1.425  0.17126   
proportion_developed      -1.608e+07  1.143e+07  -1.406  0.17667   
proportion_impervious      1.608e+07  1.143e+07   1.406  0.17667   
proportion_developed_high  2.565e+02  1.391e+02   1.845  0.08158 . 
proportion_developed_med  -2.397e+02  7.994e+01  -2.998  0.00772 **
proportion_developed_low   3.920e+02  1.214e+02   3.229  0.00465 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.005005866)

    Null deviance: 0.204609  on 23  degrees of freedom
Residual deviance: 0.090106  on 18  degrees of freedom
AIC: -51.927

Number of Fisher Scoring iterations: 2

[1] "shannon_div_mod"
Shannon ~ proportion_developed + proportion_developed_low + proportion_cultivated_crops

Call:
glm(formula = shannon_div_step$formula, data = meta)

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     7.439      1.914   3.888 0.000915 ***
proportion_developed         -387.751    139.170  -2.786 0.011400 *  
proportion_developed_low     1086.540    381.090   2.851 0.009872 ** 
proportion_cultivated_crops -1809.653    721.442  -2.508 0.020857 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.4986798)

    Null deviance: 19.4024  on 23  degrees of freedom
Residual deviance:  9.9736  on 20  degrees of freedom
AIC: 57.034

Number of Fisher Scoring iterations: 2

[1] "uniquetaxa_mod"
proportion_rare_taxa ~ proportion_developed + proportion_impervious + 
    proportion_developed_high + proportion_developed_med + proportion_developed_low

Call:
glm(formula = uniquetaxa_step$formula, data = meta)

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.240e+00  8.701e-01   1.425  0.17126   
proportion_developed      -1.608e+07  1.143e+07  -1.406  0.17667   
proportion_impervious      1.608e+07  1.143e+07   1.406  0.17667   
proportion_developed_high  2.565e+02  1.391e+02   1.845  0.08158 . 
proportion_developed_med  -2.397e+02  7.994e+01  -2.998  0.00772 **
proportion_developed_low   3.920e+02  1.214e+02   3.229  0.00465 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.005005866)

    Null deviance: 0.204609  on 23  degrees of freedom
Residual deviance: 0.090106  on 18  degrees of freedom
AIC: -51.927

Number of Fisher Scoring iterations: 2

