Habilidade competitiva
Aplicações continuas de mesmos fungicidas exercem presão de seleção sob as populações fúngicas presentes. A frequencias dos isolados integrantes da população vem sendo modificadas. Isolados com caracteristicas que permite sobreviver e se reproduzir nessa condição (“resistentes”) aumentam sua proporção.
O objetivo de este trabalho é verficar se retirando a pressão de seleção (uso continuo de um mesmo fungicida) a frequencia de isolados original da populaçao pode ser reestabelecida.
Para isso foram feitos tres experimentos (por duplicado) inoculando (concentração padrao )frutos de pessegos com um isolado resistente (SP09) e um isolado sensivel (PR09) ao longo de 5 transferencias.
Experimento 1 e 2: as unidades experimentales foram frutos de pessego frescos (variedade dorado). No exp1 foi avaliado unidades formadoras de colonia e no exp2 foi medida a germinação dos esporos dos conidios em meio de cultura com BDA com ou sem fungicida.
Experimento 3: as unidades experimentales foram pedaços de pessego enlatados. A variavel medida foi germinação dos esporos dos conidios em meio de cultura com BDA com ou sem fungicida.
Um objetivo secundario do trabalho foi testar se as diferentes metodologias conduzem a conclusões semelhantes.
1- Unidades formadoras de colonias
- Dataset
## exp rep subrep bda0 f0 bda1 f1 bda2 f2 bda3 f3 bda4 f4 bda5 f5
## 1 1 1 1 NA 42 65 22 NA 50 43 NA 28 11 47 NA
## 2 1 1 2 NA 46 58 23 77 75 42 NA 42 6 45 26
## 3 1 1 3 14 60 78 28 81 59 34 35 42 NA 45 29
## 4 1 1 4 14 39 123 27 82 62 50 38 NA 22 32 35
## 5 1 1 5 24 46 104 21 83 23 55 45 42 29 37 26
## 6 1 2 1 15 30 44 NA 48 31 42 NA 59 28 NA 25- Manipulating & summarizing
## uf_bda uf_f
## 38.31373 32.92500## exp rep subrep transf uf_bda uf_f
## 1 1 1 1 0 NA 42
## 2 1 1 2 0 NA 46
## 3 1 1 3 0 14 60
## 4 1 1 4 0 14 39
## 5 1 1 5 0 24 46
## 6 1 2 1 0 15 30## exp rep transf uf_bda uf_f
## 1 1 1 0 17.33333 48.33333
## 2 2 1 0 32.00000 6.00000
## 3 1 2 0 17.50000 55.50000
## 4 1 3 0 43.66667 34.00000
## 5 2 3 0 38.00000 15.25000
## 6 1 4 0 67.00000 44.00000- Explore data
- Opcoes graficas
## transf N resp1 sd se ci
## 1 0 11 0.5574234 0.2493662 0.07518674 0.16752651
## 2 1 10 0.5452725 0.3099769 0.09802331 0.22174413
## 3 2 12 0.8524925 0.1118509 0.03228858 0.07106668
## 4 3 11 0.7914081 0.1839360 0.05545878 0.12356986
## 5 4 12 0.6748809 0.2551326 0.07365043 0.16210351
## 6 5 10 0.7912357 0.1357157 0.04291707 0.09708516
- ANOVA
## Analysis of Variance Table
##
## Response: resp1
## Df Sum Sq Mean Sq F value Pr(>F)
## transf 4 0.63560 0.15890 5.1361 0.001549 **
## exp 1 0.33612 0.33612 10.8645 0.001828 **
## Residuals 49 1.51596 0.03094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $`Analysis of variance`
## df type III SS mean square F value p>F
## treatments 4 0.5673 0.1418 4.5838 0.0032
## blocks 1 0.3361 0.3361 10.8645 0.0018
## Residuals 49 1.5160 0.0309 - -
##
## $`Adjusted means`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 2 0.8525 0.0508 a a a a a
## 2 5 0.7912 0.0556 a a ab ab a
## 3 3 0.7843 0.0531 a a ab ab a
## 4 4 0.6749 0.0508 ab ab bc bc b
## 5 1 0.5610 0.0558 b b c c b
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 2 - 5 0.0613 0.9251 0.4196 0.4196 0.4196
## 2 2 - 3 0.0682 0.8846 0.6253 0.3879 0.3579
## 3 2 - 4 0.1776 0.1139 0.0771 0.0264 0.0170
## 4 2 - 1 0.2915 0.0029 0.0029 0.0007 0.0003
## 5 5 - 3 0.0069 1.0000 0.9289 0.9289 0.9289
## 6 5 - 4 0.1163 0.5395 0.2795 0.1512 0.1290
## 7 5 - 1 0.2302 0.0400 0.0261 0.0088 0.0052
## 8 3 - 4 0.1094 0.5746 0.1430 0.1430 0.1430
## 9 3 - 1 0.2233 0.0424 0.0152 0.0076 0.0056
## 10 4 - 1 0.1139 0.5615 0.1376 0.1376 0.1376
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.0576
## p.value Bartlett test 0.0115
## coefficient of variation (%) 23.9500
## first value most discrepant 42.0000
## second value most discrepant 5.0000
## third value most discrepant 7.0000## Analysis of Variance Table
##
## Response: logit
## Df Sum Sq Mean Sq F value Pr(>F)
## transf 4 16.482 4.1206 1.8088 0.14219
## exp 1 20.304 20.3041 8.9130 0.00441 **
## Residuals 49 111.624 2.2780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
GLM
## Analysis of Deviance Table
##
## Model: quasibinomial, link: logit
##
## Response: resp1
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev F Pr(>F)
## NULL 54 13.8853
## transf 4 3.1876 50 10.6977 4.8632 0.002207 **
## exp 1 1.8589 49 8.8389 11.3441 0.001481 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Quasi-binomial model
##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glm(formula = resp1 ~ transf + exp, family = quasibinomial, data = subset(da1,
## !transf == 0))
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 2 - 1 == 0 1.54121 0.42454 3.630 0.00283 **
## 3 - 1 == 0 1.06864 0.40299 2.652 0.08007 .
## 4 - 1 == 0 0.48379 0.36627 1.321 1.00000
## 5 - 1 == 0 1.10798 0.41397 2.676 0.07440 .
## 3 - 2 == 0 -0.47258 0.45231 -1.045 1.00000
## 4 - 2 == 0 -1.05742 0.41994 -2.518 0.11801
## 5 - 2 == 0 -0.43323 0.46195 -0.938 1.00000
## 4 - 3 == 0 -0.58485 0.39750 -1.471 1.00000
## 5 - 3 == 0 0.03934 0.44222 0.089 1.00000
## 5 - 4 == 0 0.62419 0.40904 1.526 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- bonferroni method)- Grafico
2- Germinação (5)
- Manipulating & summarizing
## exp rep subrep transf ger_bda ger_f
## 1 1 1 1 0 84 45
## 3 1 1 3 0 91 40
## 4 1 2 1 0 87 25
## 5 1 2 2 0 74 43
## 6 1 2 3 0 84 35
## 7 1 3 1 0 80 32- Explore data
- ANOVA
## Analysis of Variance Table
##
## Response: resp1
## Df Sum Sq Mean Sq F value Pr(>F)
## transf 5 0.84352 0.168704 13.3824 1.237e-08 ***
## exp 1 0.03336 0.033356 2.6459 0.1093
## Residuals 57 0.71857 0.012606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: logit
## Df Sum Sq Mean Sq F value Pr(>F)
## transf 5 37.042 7.4083 14.4443 3.929e-09 ***
## exp 1 0.387 0.3875 0.7555 0.3884
## Residuals 57 29.235 0.5129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## transf N resp1 sd se ci
## 1 0 6 0.5715083 0.16624617 0.06786971 0.17446465
## 2 1 10 0.8821282 0.07595326 0.02401853 0.05433369
## 3 2 10 0.8175186 0.06594609 0.02085399 0.04717499
## 4 3 12 0.8173070 0.11807492 0.03408529 0.07502122
## 5 4 12 0.6264021 0.19233674 0.05552283 0.12220493
## 6 5 12 0.6583245 0.09020121 0.02603885 0.05731112
## $`Analysis of variance`
## df type III SS mean square F value p>F
## treatments 5 24.5709 4.9142 14.0788 <0.001
## blocks 1 0.4001 0.4001 1.1462 0.289
## Residuals 55 19.1976 0.3490 - -
##
## $`Adjusted means`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 1 2.1426 0.1868 a a a a a
## 2 3 1.6049 0.1705 a b b b b
## 3 2 1.5546 0.1874 a ab b b b
## 4 5 0.6760 0.1705 b c c c c
## 5 4 0.5875 0.1705 b c c c c
## 6 0 0.3156 0.2412 b c c c c
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 1 - 3 0.5377 0.2895 0.0380 0.0380 0.0380
## 2 1 - 2 0.5880 0.2445 0.0763 0.0389 0.0304
## 3 1 - 5 1.4666 0.0000 0.0000 0.0000 0.0000
## 4 1 - 4 1.5551 0.0000 0.0000 0.0000 0.0000
## 5 1 - 0 1.8270 0.0000 0.0000 0.0000 0.0000
## 6 3 - 2 0.0503 1.0000 0.8434 0.8434 0.8434
## 7 3 - 5 0.9289 0.0040 0.0009 0.0004 0.0003
## 8 3 - 4 1.0174 0.0012 0.0005 0.0002 0.0001
## 9 3 - 0 1.2893 0.0008 0.0005 0.0001 0.0001
## 10 2 - 5 0.8786 0.0125 0.0010 0.0010 0.0010
## 11 2 - 4 0.9671 0.0044 0.0010 0.0005 0.0003
## 12 2 - 0 1.2390 0.0021 0.0009 0.0003 0.0002
## 13 5 - 4 0.0885 0.9991 0.7150 0.7150 0.7150
## 14 5 - 0 0.3604 0.8252 0.4465 0.2560 0.2276
## 15 4 - 0 0.2719 0.9396 0.3613 0.3613 0.3613
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.2020
## p.value Bartlett test 0.1207
## coefficient of variation (%) 50.0900
## first value most discrepant 28.0000
## second value most discrepant 10.0000
## third value most discrepant 46.0000## transf let me se
## 1 0 b 0.3156 0.2412
## 2 1 a 2.1426 0.1868
## 3 2 a 1.5546 0.1874
## 4 3 a 1.6049 0.1705
## 5 4 b 0.5875 0.1705
## 6 5 b 0.6760 0.1705
- Gráfico
3- Germinação (6)
- Manipulating & summarizing
## exp rep subrep transf ger_bda ger_f
## 1 1 1 1 0 98 53
## 2 1 1 2 0 96 48
## 3 1 1 3 0 92 42
## 4 1 2 1 0 100 55
## 5 1 2 2 0 96 41
## 6 1 2 3 0 95 52- Explore data
- ANOVA
## Analysis of Variance Table
##
## Response: resp
## Df Sum Sq Mean Sq F value Pr(>F)
## transf 5 0.38708 0.077416 16.8206 2.662e-10 ***
## exp 1 0.00013 0.000125 0.0273 0.8694
## Residuals 59 0.27155 0.004602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## transf N resp sd se ci
## 1 0 6 0.5161783 0.04621327 0.01886649 0.04849785
## 2 1 11 0.6095882 0.04337389 0.01307772 0.02913897
## 3 2 12 0.6311307 0.08305206 0.02397506 0.05276876
## 4 3 11 0.7748330 0.04228162 0.01274839 0.02840518
## 5 4 12 0.7383695 0.05485486 0.01583524 0.03485312
## 6 5 12 0.6573364 0.07458186 0.02152993 0.04738705
## $`Analysis of variance`
## df type III SS mean square F value p>F
## treatments 5 0.3771 0.0754 23.7631 <0.001
## blocks 1 0.0004 0.0004 0.1185 0.732
## Residuals 57 0.1809 0.0032 - -
##
## $`Adjusted means`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 3 0.7746 0.0170 a a a a a
## 2 4 0.7384 0.0163 a a a a a
## 3 5 0.6573 0.0163 b b b b b
## 4 2 0.6311 0.0163 b b b bc b
## 5 1 0.6098 0.0170 b b b c b
## 6 0 0.5162 0.0230 c c c d c
##
## $`Multiple comparison test`
## pair contrast p(tukey) p(snk) p(duncan) p(t)
## 1 3 - 4 0.0362 0.6423 0.1298 0.1298 0.1298
## 2 3 - 5 0.1173 0.0001 0.0000 0.0000 0.0000
## 3 3 - 2 0.1435 0.0000 0.0000 0.0000 0.0000
## 4 3 - 1 0.1648 0.0000 0.0000 0.0000 0.0000
## 5 3 - 0 0.2584 0.0000 0.0000 0.0000 0.0000
## 6 4 - 5 0.0811 0.0106 0.0009 0.0009 0.0009
## 7 4 - 2 0.1073 0.0003 0.0001 0.0000 0.0000
## 8 4 - 1 0.1286 0.0000 0.0000 0.0000 0.0000
## 9 4 - 0 0.2222 0.0000 0.0000 0.0000 0.0000
## 10 5 - 2 0.0262 0.8640 0.2605 0.2605 0.2605
## 11 5 - 1 0.0475 0.3459 0.1174 0.0605 0.0484
## 12 5 - 0 0.1411 0.0001 0.0000 0.0000 0.0000
## 13 2 - 1 0.0213 0.9438 0.3696 0.3696 0.3696
## 14 2 - 0 0.1149 0.0019 0.0004 0.0002 0.0001
## 15 1 - 0 0.0936 0.0213 0.0018 0.0018 0.0018
##
## $`Residual analysis`
## values
## p.value Shapiro-Wilk test 0.3771
## p.value Bartlett test 0.1480
## coefficient of variation (%) 8.4500
## first value most discrepant 27.0000
## second value most discrepant 60.0000
## third value most discrepant 53.0000## transf let me se
## 1 0 c 0.5162 0.0230
## 2 1 b 0.6098 0.0170
## 3 2 b 0.6311 0.0163
## 4 3 a 0.7746 0.0170
## 5 4 a 0.7384 0.0163
## 6 5 b 0.6573 0.0163- Grafico
Todos os graficos
Testar correlações entre experimentos
## y.ufc y.ger5 y.ger6
## y.ufc 1.00 0.16 0.57
## y.ger5 0.16 1.00 0.15
## y.ger6 0.57 0.15 1.00
##
## n= 6
##
##
## P
## y.ufc y.ger5 y.ger6
## y.ufc 0.7613 0.2340
## y.ger5 0.7613 0.7759
## y.ger6 0.2340 0.7759