PCA STATISTICS for All species PCA Ordination type: Principal Component Analysis Centering by OLS mean Orthogonal projection of OLS residuals Number of observations: 128 Number of vectors 86 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 0.007465681 0.002065063 0.001503377 0.0006412031 Proportion of Variance 0.613478767 0.169692841 0.123537306 0.0526897000 Cumulative Proportion 0.613478767 0.783171608 0.906708914 0.9593986143 MANOVA for dataset of all species Analysis of Variance, using Residual Randomization Permutation procedure: Randomization of raw values (residuals of mean) Number of permutations: 1000 Estimation method: Ordinary Least Squares Sums of Squares and Cross-products: Type I Effect sizes (Z) based on F distributions Df SS MS Rsq F Z Pr(>F) associated 2 0.24481 0.122404 0.1584 11.763 4.7981 0.001 ** Residuals 125 1.30071 0.010406 0.8416 Total 127 1.54552 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: procD.lm(f1 = coords ~ associated, iter = 999, RRPP = FALSE, data = gdf, print.progress = FALSE) PCA Statistics for without neochavesia Ordination type: Principal Component Analysis Centering by OLS mean Orthogonal projection of OLS residuals Number of observations: 100 Number of vectors 86 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 0.004334688 0.001610909 0.0007204899 0.0002868472 Proportion of Variance 0.598536827 0.222435497 0.0994857725 0.0396080636 Cumulative Proportion 0.598536827 0.820972324 0.9204580967 0.9600661603 MANOVA for without neochavesia Analysis of Variance, using Residual Randomization Permutation procedure: Randomization of raw values (residuals of mean) Number of permutations: 1000 Estimation method: Ordinary Least Squares Sums of Squares and Cross-products: Type I Effect sizes (Z) based on F distributions Df SS MS Rsq F Z Pr(>F) associated 2 0.12706 0.063531 0.17722 10.447 4.4196 0.001 ** Residuals 97 0.58991 0.006082 0.82278 Total 99 0.71697 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Call: procD.lm(f1 = coords ~ associated, iter = 999, RRPP = FALSE, data = gdf2, print.progress = FALSE) Linear Regression stats for set including Xenococcids: Call: lm(formula = tro.L ~ body.L, data = lin_data) Trochanter Length Residuals: Min 1Q Median 3Q Max -19.578 -7.774 -0.815 5.717 34.201 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 30.63408 3.66090 8.368 1.30e-13 *** body.L 0.01416 0.00276 5.131 1.14e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.37 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1811, Adjusted R-squared: 0.1743 F-statistic: 26.33 on 1 and 119 DF, p-value: 1.135e-06 Trochanter Width/body length Call: lm(formula = tro.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -14.173 -5.306 -1.176 3.452 29.169 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 18.407332 2.384846 7.718 3.89e-12 *** body.L 0.008941 0.001802 4.962 2.33e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.46 on 120 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.1702, Adjusted R-squared: 0.1633 F-statistic: 24.62 on 1 and 120 DF, p-value: 2.329e-06 Femur length: Call: lm(formula = fem.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -53.749 -25.753 -3.656 14.162 118.026 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 55.017977 10.759590 5.113 1.20e-06 *** body.L 0.042096 0.008155 5.162 9.71e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 34.13 on 121 degrees of freedom (6 observations deleted due to missingness) Multiple R-squared: 0.1805, Adjusted R-squared: 0.1737 F-statistic: 26.65 on 1 and 121 DF, p-value: 9.71e-07 Femur Width Call: lm(formula = fem.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -16.856 -6.418 -1.455 5.250 33.700 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 26.089066 3.027114 8.618 3.08e-14 *** body.L 0.012510 0.002294 5.453 2.67e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.602 on 121 degrees of freedom (6 observations deleted due to missingness) Multiple R-squared: 0.1972, Adjusted R-squared: 0.1906 F-statistic: 29.73 on 1 and 121 DF, p-value: 2.667e-07 Tibia Length Call: lm(formula = tib.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -45.780 -15.881 -3.714 8.969 86.508 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 38.500207 7.235247 5.321 4.92e-07 *** body.L 0.031971 0.005469 5.846 4.50e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 22.78 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2231, Adjusted R-squared: 0.2166 F-statistic: 34.18 on 1 and 119 DF, p-value: 4.502e-08 Tibia Width Call: lm(formula = tib.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -10.913 -4.707 -1.928 4.250 22.918 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 20.566725 2.067264 9.949 < 2e-16 *** body.L 0.004498 0.001563 2.879 0.00474 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 6.507 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.0651, Adjusted R-squared: 0.05725 F-statistic: 8.287 on 1 and 119 DF, p-value: 0.004736 Tarsus Length Call: lm(formula = tar.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -27.298 -11.401 -0.128 7.604 50.295 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 26.522185 4.903271 5.409 3.32e-07 *** body.L 0.025404 0.003706 6.855 3.38e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15.43 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.2831, Adjusted R-squared: 0.2771 F-statistic: 46.99 on 1 and 119 DF, p-value: 3.383e-10 Tarsus Width Call: lm(formula = tar.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -10.0050 -3.6800 -0.6493 2.8358 19.7584 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 15.809258 1.721882 9.181 1.62e-15 *** body.L 0.003246 0.001301 2.494 0.014 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.42 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.04967, Adjusted R-squared: 0.04169 F-statistic: 6.22 on 1 and 119 DF, p-value: 0.01401 Tarsal Claw Length lm(formula = tclaw.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -16.779 -6.662 -3.060 2.522 52.586 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 21.788901 3.969441 5.489 2.32e-07 *** body.L 0.008137 0.003007 2.706 0.00782 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.59 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.05796, Adjusted R-squared: 0.05004 F-statistic: 7.321 on 1 and 119 DF, p-value: 0.007816 Tarsal Claw Width lm(formula = tclaw.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -4.607 -1.872 -0.450 1.069 10.814 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.1181396 0.8436954 7.252 4.53e-11 *** body.L 0.0023205 0.0006392 3.630 0.000419 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.675 on 119 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.0997, Adjusted R-squared: 0.09214 F-statistic: 13.18 on 1 and 119 DF, p-value: 0.0004187 DATASETS W/O Xenococcids: Trochanter Length Call: lm(formula = tro.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -14.9168 -6.6812 -0.1129 5.8887 19.0017 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 26.355727 2.828752 9.317 1.69e-14 *** body.L 0.013766 0.002062 6.677 2.70e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.871 on 82 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.3522, Adjusted R-squared: 0.3443 F-statistic: 44.58 on 1 and 82 DF, p-value: 2.696e-09 Trochanter Width Call: lm(formula = tro.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -11.744 -4.102 -1.230 4.031 16.683 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.620141 2.080349 8.47 7.51e-13 *** body.L 0.008080 0.001522 5.31 8.99e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.845 on 83 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.2536, Adjusted R-squared: 0.2446 F-statistic: 28.2 on 1 and 83 DF, p-value: 8.985e-07 Femur Length Call: lm(formula = fem.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -38.226 -14.022 -0.145 14.414 42.228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 41.707186 6.756953 6.172 2.28e-08 *** body.L 0.040321 0.004964 8.122 3.46e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19.34 on 84 degrees of freedom (6 observations deleted due to missingness) Multiple R-squared: 0.4399, Adjusted R-squared: 0.4332 F-statistic: 65.97 on 1 and 84 DF, p-value: 3.456e-12 Femur Width Call: lm(formula = fem.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -13.8095 -5.5668 -0.5308 4.4780 19.2712 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 26.103339 2.738680 9.531 5.06e-15 *** body.L 0.010800 0.002012 5.367 6.96e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.838 on 84 degrees of freedom (6 observations deleted due to missingness) Multiple R-squared: 0.2554, Adjusted R-squared: 0.2465 F-statistic: 28.81 on 1 and 84 DF, p-value: 6.962e-07 Tibia Length Call: lm(formula = tib.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -38.608 -13.596 -3.027 11.109 45.356 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 35.035165 5.727278 6.117 3.00e-08 *** body.L 0.028996 0.004204 6.897 9.71e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 16.38 on 83 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.3643, Adjusted R-squared: 0.3566 F-statistic: 47.56 on 1 and 83 DF, p-value: 9.707e-10 Tibia Width Call: lm(formula = tib.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -8.806 -3.373 -1.271 1.888 16.376 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 19.623506 1.823561 10.761 < 2e-16 *** body.L 0.003564 0.001339 2.663 0.00931 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.214 on 83 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.0787, Adjusted R-squared: 0.0676 F-statistic: 7.09 on 1 and 83 DF, p-value: 0.009308 Tarsus Length Call: lm(formula = tar.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -23.498 -8.920 2.737 8.104 20.655 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 21.539879 3.803763 5.663 2.08e-07 *** body.L 0.026079 0.002792 9.339 1.37e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 10.88 on 83 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.5124, Adjusted R-squared: 0.5065 F-statistic: 87.23 on 1 and 83 DF, p-value: 1.367e-14 Tarsus Width Call: lm(formula = tar.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -7.9555 -2.8825 -0.6055 2.4269 13.5966 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 14.384278 1.380774 10.418 < 2e-16 *** body.L 0.002745 0.001014 2.708 0.00822 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.948 on 83 degrees of freedom (7 observations deleted due to missingness) Multiple R-squared: 0.08117, Adjusted R-squared: 0.0701 F-statistic: 7.332 on 1 and 83 DF, p-value: 0.008222 Tarsal Claw Length Call: lm(formula = tclaw.L ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -12.465 -3.342 0.022 2.569 13.184 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 19.583977 1.847782 10.599 < 2e-16 *** body.L 0.005740 0.001356 4.233 5.97e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.281 on 82 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1793, Adjusted R-squared: 0.1693 F-statistic: 17.92 on 1 and 82 DF, p-value: 5.974e-05 Tarsal Claw Width Call: lm(formula = tclaw.W ~ body.L, data = lin_data) Residuals: Min 1Q Median 3Q Max -3.0488 -1.3571 -0.1952 1.4657 3.6506 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.0267176 0.6197448 9.725 2.62e-15 *** body.L 0.0017360 0.0004548 3.817 0.000261 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.771 on 82 degrees of freedom (8 observations deleted due to missingness) Multiple R-squared: 0.1509, Adjusted R-squared: 0.1405 F-statistic: 14.57 on 1 and 82 DF, p-value: 0.0002609 Ostiole DATA (Regressions) Anterior Ostiole regression Call: lm(formula = A.ost.L ~ A.Body.L, data = ant.ost.data) Residuals: Min 1Q Median 3Q Max -23.1213 -7.6056 -0.6869 6.7464 27.5642 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.403236 6.447575 1.303 0.198 A.Body.L 0.025345 0.004376 5.792 4.61e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.63 on 50 degrees of freedom Multiple R-squared: 0.4015, Adjusted R-squared: 0.3895 F-statistic: 33.54 on 1 and 50 DF, p-value: 4.605e-07 Posterior Ostiole Regression Residuals: Min 1Q Median 3Q Max -26.205 -8.315 -0.726 5.281 36.990 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 18.594639 5.191231 3.582 0.000646 *** P.Body.L 0.026337 0.003862 6.820 3.38e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.29 on 66 degrees of freedom Multiple R-squared: 0.4134, Adjusted R-squared: 0.4045 F-statistic: 46.51 on 1 and 66 DF, p-value: 3.384e-09