Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: Alive ~ AP_treatment * Species + (1 | Species:Composition) Data: effects_APtreatment_data_S_second_season AIC BIC logLik deviance df.resid 937.2 972.4 -461.6 923.2 1125 Scaled residuals: Min 1Q Median 3Q Max -6.4795 -0.3621 0.3316 0.3989 2.7620 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.4875 0.6982 Number of obs: 1132, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.54269 0.23191 6.652 2.89e-11 *** AP_treatment1 -0.27022 0.09402 -2.874 0.004052 ** Species1 -1.81065 0.31500 -5.748 9.02e-09 *** Species2 1.39185 0.34229 4.066 4.78e-05 *** AP_treatment1:Species1 -0.39061 0.11673 -3.346 0.000819 *** AP_treatment1:Species2 0.09669 0.15092 0.641 0.521730 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 -0.030 Species1 -0.102 0.032 Species2 0.139 -0.053 -0.524 AP_trtm1:S1 0.021 -0.368 0.001 0.019 AP_trtm1:S2 -0.039 0.360 0.022 -0.045 -0.562 chisq ratio rdf p 1085.6050710 0.9649823 1125.0000000 0.7956883 $`Species:Composition` Analysis of Deviance Table (Type III Wald chisquare tests) Response: Alive Chisq Df Pr(>Chisq) (Intercept) 44.2520 1 2.887e-11 *** AP_treatment 8.2604 1 0.004052 ** Species 34.5696 2 3.114e-08 *** AP_treatment:Species 13.4409 2 0.001206 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: Alive ~ AP_treatment * Species + (1 | Species:Composition) Data: effects_APtreatment_data_NE_second_season AIC BIC logLik deviance df.resid 869.2 904.2 -427.6 855.2 1086 Scaled residuals: Min 1Q Median 3Q Max -8.1562 0.1718 0.3480 0.4971 0.6826 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.62 0.7874 Number of obs: 1093, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.268489 0.280802 8.079 6.55e-16 *** AP_treatment1 -0.094813 0.095236 -0.996 0.31946 Species1 -1.028487 0.368919 -2.788 0.00531 ** Species2 0.163832 0.370426 0.442 0.65829 AP_treatment1:Species1 0.009926 0.119989 0.083 0.93407 AP_treatment1:Species2 0.376481 0.135499 2.778 0.00546 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 -0.035 Species1 -0.200 0.023 Species2 -0.009 0.086 -0.417 AP_trtm1:S1 0.024 -0.327 -0.023 -0.066 AP_trtm1:S2 0.086 0.017 -0.063 0.030 -0.341 chisq ratio rdf p 996.0970000 0.9172164 1086.0000000 0.9756156 $`Species:Composition` Analysis of Deviance Table (Type III Wald chisquare tests) Response: Alive Chisq Df Pr(>Chisq) (Intercept) 65.2637 1 6.552e-16 *** AP_treatment 0.9911 1 0.31946 Species 8.3976 2 0.01501 * AP_treatment:Species 8.9210 2 0.01156 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: Alive ~ AP_treatment + Species + (1 | Species:Composition) Data: effects_APtreatment_data_NW_second_season AIC BIC logLik deviance df.resid 1322.0 1349.9 -656.0 1312.0 1982 Scaled residuals: Min 1Q Median 3Q Max -4.0231 0.2486 0.3240 0.3909 0.4520 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0 0 Number of obs: 1987, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.19447 0.07693 28.524 < 2e-16 *** AP_treatment1 -0.33283 0.07599 -4.380 1.19e-05 *** Species1 0.25679 0.10996 2.335 0.01952 * Species2 -0.27352 0.09984 -2.740 0.00615 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 AP_tretmnt1 -0.251 Species1 0.120 -0.012 Species2 -0.147 0.001 -0.493 optimizer (Nelder_Mead) convergence code: 0 (OK) boundary (singular) fit: see help('isSingular') chisq ratio rdf p 1992.442994 1.005269 1982.000000 0.430084 $`Species:Composition` Analysis of Deviance Table (Type II tests) Response: Alive LR Chisq Df Pr(>Chisq) AP_treatment 19.9421 1 7.982e-06 *** Species 8.8511 2 0.01197 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 chisq ratio rdf p 757.7680583 0.9185067 825.0000000 0.9540863 $`Species:Composition` pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: Weevil Chisq Df Pr(>Chisq) (Intercept) 5.0713 1 0.02432 * AP_treatment 2.8118 1 0.09357 . Species 32.2904 2 9.733e-08 *** AP_treatment:Species 27.7397 2 9.471e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: browsed ~ AP_treatment * Species + (1 | Species:Composition) Data: SouthAlive AIC BIC logLik deviance df.resid 668.7 701.7 -327.3 654.7 825 Scaled residuals: Min 1Q Median 3Q Max -4.1266 -0.3935 -0.1309 0.4271 4.2635 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.636 0.7975 Number of obs: 832, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.0703 0.2875 -3.722 0.000198 *** AP_treatment1 -0.1373 0.1411 -0.973 0.330688 Species1 -1.7488 0.4344 -4.026 5.67e-05 *** Species2 -0.8871 0.3780 -2.347 0.018947 * AP_treatment1:Species1 1.0301 0.2486 4.143 3.43e-05 *** AP_treatment1:Species2 -0.5516 0.1708 -3.229 0.001242 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 -0.100 Species1 0.265 -0.159 Species2 -0.110 0.162 -0.564 AP_trtm1:S1 -0.139 0.631 -0.178 0.069 AP_trtm1:S2 0.173 -0.436 0.085 -0.011 -0.740 chisq ratio rdf p 708.7773926 0.8591241 825.0000000 0.9986106 $`Species:Composition` pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: browsed Chisq Df Pr(>Chisq) (Intercept) 13.8537 1 0.0001976 *** AP_treatment 0.9462 1 0.3306877 Species 47.5214 2 4.796e-11 *** AP_treatment:Species 17.2235 2 0.0001820 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: browsed ~ AP_treatment + Species + (1 | Species:Composition) Data: NWAlive AIC BIC logLik deviance df.resid 1344.8 1372.2 -667.4 1334.8 1772 Scaled residuals: Min 1Q Median 3Q Max -0.8532 -0.5030 -0.2636 -0.1950 7.4689 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.3509 0.5923 Number of obs: 1777, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.07585 0.19728 -10.522 < 2e-16 *** AP_treatment1 0.21981 0.07058 3.115 0.00184 ** Species1 0.86509 0.26738 3.235 0.00121 ** Species2 -1.18495 0.29379 -4.033 5.5e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 AP_tretmnt1 -0.043 Species1 -0.107 0.007 Species2 0.169 -0.009 -0.541 chisq ratio rdf p 1713.6730094 0.9670841 1772.0000000 0.8363330 $`Species:Composition` pdf 2 Analysis of Deviance Table (Type II Wald chisquare tests) Response: browsed Chisq Df Pr(>Chisq) AP_treatment 9.7007 1 0.0018420 ** Species 17.8364 2 0.0001339 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Height_cm ~ AP_treatment + Species + (1 | Species:Composition) Data: SouthVital REML criterion at convergence: 2595.3 Scaled residuals: Min 1Q Median 3Q Max -4.7475 -0.5055 -0.0649 0.4950 4.3637 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 94.99 9.746 Residual 142.44 11.935 Number of obs: 331, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 58.2494 2.9491 6.1324 19.752 8.75e-07 *** AP_treatment1 -1.1304 0.7079 322.0660 -1.597 0.11132 Species1 -38.2858 4.1774 6.1985 -9.165 7.87e-05 *** Species2 -16.6359 4.1083 5.8011 -4.049 0.00722 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 AP_tretmnt1 0.046 Species1 0.007 0.035 Species2 -0.040 -0.006 -0.482 pdf 2 pdf 2 Analysis of Deviance Table (Type II Wald chisquare tests) Response: Height_cm Chisq Df Pr(>Chisq) AP_treatment 2.5494 1 0.1103 Species 177.4281 2 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type II Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 361.5 361.5 1 323.64 2.5376 0.1121 Species 25194.9 12597.4 2 8.94 88.4325 1.282e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Height_cm ~ AP_treatment * Species + (1 | Species:Composition) Data: NEVital REML criterion at convergence: 4391.5 Scaled residuals: Min 1Q Median 3Q Max -3.4521 -0.6056 -0.0505 0.5491 6.2381 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.00 0.000 Residual 68.16 8.256 Number of obs: 623, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 48.9583 0.3815 617.0000 128.346 < 2e-16 *** AP_treatment1 -1.5782 0.3815 617.0000 -4.137 4e-05 *** Species1 -16.5166 0.4954 617.0000 -33.339 < 2e-16 *** Species2 -12.6368 0.4683 617.0000 -26.987 < 2e-16 *** AP_treatment1:Species1 -1.4468 0.4954 617.0000 -2.920 0.00362 ** AP_treatment1:Species2 0.2568 0.4683 617.0000 0.548 0.58359 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 -0.036 Species1 -0.241 0.056 Species2 -0.402 0.030 -0.121 AP_trtm1:S1 0.056 -0.241 0.000 -0.046 AP_trtm1:S2 0.030 -0.402 -0.046 -0.024 -0.121 optimizer (nloptwrap) convergence code: 0 (OK) boundary (singular) fit: see help('isSingular') pdf 2 pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: Height_cm Chisq Df Pr(>Chisq) (Intercept) 16472.7109 1 < 2.2e-16 *** AP_treatment 17.1183 1 3.512e-05 *** Species 2089.1669 2 < 2.2e-16 *** AP_treatment:Species 8.5664 2 0.0138 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type III Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 1163 1163 1 615.87 17.0682 4.105e-05 *** Species 132493 66247 2 6.89 962.4035 3.709e-09 *** AP_treatment:Species 580 290 2 615.83 4.2581 0.01457 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Height_cm ~ AP_treatment * Species + (1 | Species:Composition) Data: NWVital REML criterion at convergence: 6259.4 Scaled residuals: Min 1Q Median 3Q Max -2.6666 -0.5993 -0.0066 0.5167 6.0643 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 1.213 1.102 Residual 57.383 7.575 Number of obs: 908, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 34.9618 0.4142 7.7074 84.408 1.03e-12 *** AP_treatment1 -2.9545 0.2519 894.2711 -11.727 < 2e-16 *** Species1 4.2066 0.5859 7.7467 7.180 0.000111 *** Species2 -4.8199 0.5860 7.7020 -8.226 4.45e-05 *** AP_treatment1:Species1 0.7345 0.3580 893.6525 2.052 0.040458 * AP_treatment1:Species2 1.2037 0.3552 894.4226 3.388 0.000734 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 0.030 Species1 0.001 0.002 Species2 0.001 -0.003 -0.501 AP_trtm1:S1 0.002 0.013 0.032 -0.014 AP_trtm1:S2 -0.003 -0.009 -0.014 0.028 -0.502 pdf 2 pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: Height_cm Chisq Df Pr(>Chisq) (Intercept) 7124.706 1 < 2.2e-16 *** AP_treatment 137.513 1 < 2.2e-16 *** Species 80.157 2 < 2.2e-16 *** AP_treatment:Species 30.339 2 2.583e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type III Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 7887.0 7887.0 1 895.04 137.445 < 2.2e-16 *** Species 4545.0 2272.5 2 8.56 39.602 4.713e-05 *** AP_treatment:Species 1739.8 869.9 2 895.04 15.160 3.352e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Diameter_mm ~ AP_treatment + Species + (1 | Species:Composition) Data: SouthVital REML criterion at convergence: 1471.1 Scaled residuals: Min 1Q Median 3Q Max -3.3565 -0.5614 -0.0203 0.5550 4.1249 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 3.297 1.816 Residual 4.568 2.137 Number of obs: 331, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 9.6747 0.5477 7.3094 17.665 2.91e-07 *** AP_treatment1 -0.0572 0.1268 322.5211 -0.451 0.65229 Species1 -2.8702 0.7756 7.3789 -3.701 0.00697 ** Species2 -0.8282 0.7637 6.9388 -1.084 0.31441 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 AP_tretmnt1 0.045 Species1 0.007 0.034 Species2 -0.037 -0.006 -0.483 pdf 2 pdf 2 Analysis of Deviance Table (Type II Wald chisquare tests) Response: Diameter_mm Chisq Df Pr(>Chisq) AP_treatment 0.2034 1 0.652 Species 24.4599 2 4.882e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type II Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 0.925 0.925 1 323.37 0.2025 0.653002 Species 111.445 55.723 2 8.95 12.1967 0.002779 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Diameter_mm ~ AP_treatment * Species + (1 | Species:Composition) Data: NEVital REML criterion at convergence: 2566.8 Scaled residuals: Min 1Q Median 3Q Max -2.7726 -0.6573 -0.0486 0.5924 3.2650 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.1632 0.4039 Residual 3.4865 1.8672 Number of obs: 623, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 9.37270 0.15247 7.92676 61.473 6.61e-12 *** AP_treatment1 -0.58028 0.08656 612.30634 -6.704 4.62e-11 *** Species1 0.78596 0.20645 7.08140 3.807 0.00651 ** Species2 -0.83998 0.20261 6.60762 -4.146 0.00490 ** AP_treatment1:Species1 -0.39625 0.11251 612.23014 -3.522 0.00046 *** AP_treatment1:Species2 0.17006 0.10615 610.88139 1.602 0.10966 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 -0.033 Species1 -0.123 0.030 Species2 -0.176 0.025 -0.333 AP_trtm1:S1 0.031 -0.239 -0.014 -0.024 AP_trtm1:S2 0.027 -0.405 -0.025 -0.020 -0.121 pdf 2 pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: Diameter_mm Chisq Df Pr(>Chisq) (Intercept) 3778.909 1 < 2.2e-16 *** AP_treatment 44.940 1 2.031e-11 *** Species 23.808 2 6.762e-06 *** AP_treatment:Species 13.804 2 0.001006 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type III Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 156.450 156.450 1 613.05 44.8731 4.764e-11 *** Species 82.546 41.273 2 8.78 11.8129 0.003240 ** AP_treatment:Species 48.053 24.026 2 612.53 6.8913 0.001097 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Linear mixed model fit by REML. t-tests use Satterthwaite's method [ lmerModLmerTest] Formula: Diameter_mm ~ AP_treatment * Species + (1 | Species:Composition) Data: NWVital REML criterion at convergence: 2852.2 Scaled residuals: Min 1Q Median 3Q Max -2.9034 -0.6124 -0.1712 0.4233 5.8823 Random effects: Groups Name Variance Std.Dev. Species:Composition (Intercept) 0.2072 0.4551 Residual 1.2927 1.1370 Number of obs: 908, groups: Species:Composition, 12 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 5.34797 0.13757 8.63830 38.875 5.30e-11 *** AP_treatment1 -0.19124 0.03783 893.11018 -5.055 5.22e-07 *** Species1 0.85789 0.19453 8.63551 4.410 0.001874 ** Species2 -0.75277 0.19457 8.64219 -3.869 0.004099 ** AP_treatment1:Species1 0.09110 0.05374 892.96041 1.695 0.090417 . AP_treatment1:Species2 0.18193 0.05334 893.14874 3.410 0.000678 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) AP_tr1 Specs1 Specs2 AP_1:S1 AP_tretmnt1 0.014 Species1 0.000 0.000 Species2 0.000 -0.001 -0.500 AP_trtm1:S1 0.000 0.013 0.014 -0.007 AP_trtm1:S2 -0.001 -0.008 -0.007 0.013 -0.502 pdf 2 pdf 2 Analysis of Deviance Table (Type III Wald chisquare tests) Response: Diameter_mm Chisq Df Pr(>Chisq) (Intercept) 1511.248 1 < 2.2e-16 *** AP_treatment 25.553 1 4.305e-07 *** Species 23.140 2 9.444e-06 *** AP_treatment:Species 27.166 2 1.262e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Type III Analysis of Variance Table with Kenward-Roger's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) AP_treatment 33.027 33.027 1 893.45 25.550 5.230e-07 *** Species 29.901 14.950 2 8.98 11.565 0.003273 ** AP_treatment:Species 35.112 17.556 2 893.45 13.581 1.548e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 R version 4.2.1 (2022-06-23) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Arch Linux Matrix products: default BLAS: /usr/lib/libopenblasp-r0.3.21.so LAPACK: /usr/lib/liblapack.so.3.10.1 locale: [1] LC_CTYPE=sv_SE.UTF-8 LC_NUMERIC=C [3] LC_TIME=sv_SE.UTF-8 LC_COLLATE=sv_SE.UTF-8 [5] LC_MONETARY=sv_SE.UTF-8 LC_MESSAGES=sv_SE.UTF-8 [7] LC_PAPER=sv_SE.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] grid stats graphics grDevices utils datasets methods [8] base other attached packages: [1] glmmTMB_1.1.4 gridExtra_2.3 sjPlot_2.8.11 multcompView_0.1-8 [5] multcomp_1.4-20 TH.data_1.1-1 MASS_7.3-57 survival_3.3-1 [9] mvtnorm_1.1-3 ggpmisc_0.5.0 ggpp_0.4.5 ggpubr_0.4.0 [13] Rmisc_1.5.1 plyr_1.8.7 lattice_0.20-45 ggthemes_4.2.4 [17] ggplot2_3.3.6 emmeans_1.8.1-1 car_3.1-0 carData_3.0-5 [21] pbkrtest_0.5.1 lmerTest_3.1-3 lme4_1.1-30 Matrix_1.5-0 [25] dplyr_1.0.10 loaded via a namespace (and not attached): [1] tidyr_1.2.1 splines_4.2.1 modelr_0.1.9 [4] datawizard_0.6.1 assertthat_0.2.1 bayestestR_0.13.0 [7] numDeriv_2016.8-1.1 pillar_1.8.1 backports_1.4.1 [10] quantreg_5.94 glue_1.6.2 digest_0.6.29 [13] RColorBrewer_1.1-3 ggsignif_0.6.3 minqa_1.2.4 [16] colorspace_2.0-3 sandwich_3.0-2 pkgconfig_2.0.3 [19] broom_1.0.1 SparseM_1.81 purrr_0.3.4 [22] xtable_1.8-4 scales_1.2.1 MatrixModels_0.5-1 [25] tibble_3.1.8 mgcv_1.8-40 farver_2.1.1 [28] generics_0.1.3 sjlabelled_1.2.0 withr_2.5.0 [31] TMB_1.9.1 cli_3.4.0 magrittr_2.0.3 [34] effectsize_0.7.0.5 estimability_1.4.1 fansi_1.0.3 [37] nlme_3.1-157 rstatix_0.7.0 tools_4.2.1 [40] lifecycle_1.0.2 stringr_1.4.1 munsell_0.5.0 [43] ggeffects_1.1.3 compiler_4.2.1 rlang_1.0.5 [46] nloptr_2.0.3 parameters_0.18.2 labeling_0.4.2 [49] boot_1.3-28 gtable_0.3.1 codetools_0.2-18 [52] abind_1.4-5 sjstats_0.18.1 DBI_1.1.3 [55] sjmisc_2.8.9 R6_2.5.1 zoo_1.8-11 [58] knitr_1.40 performance_0.9.2 utf8_1.2.2 [61] insight_0.18.4 stringi_1.7.8 parallel_4.2.1 [64] Rcpp_1.0.9 vctrs_0.4.1 tidyselect_1.1.2 [67] xfun_0.32