INTRODUCTION The Need for Statistics Types of Data The Use of Computers in Statistics PROBABILITY AND DISTRIBUTIONS Probability Populations and Samples Means and Variances The Normal Distribution Sampling Distributions ESTIMATION AND HYPOTHESIS TESTING Estimation of the Population Mean Testing Hypotheses about the Population Mean Population Variance Unknown Comparison of Samples A Pooled Estimate of Variance A SIMPLE EXPERIMENT Randomization and Replication Analysis of a Completely Randomized Design with Two Treatments A Completely Randomized Design with Several Treatments Testing Overall Variation Between the Treatments CONTROL OF RANDOM VARIATION BY BLOCKING Local Control of Variation Analysis of a Randomized Block Design Meaning of the Error Mean Square Latin Square Designs Multiple Latin Squares Design The Benefit of Blocking and the Use of Natural Blocks PARTICULAR QUESTIONS ABOUT TREATMENTS Treatment Structure Treatment Contrasts Factorial Treatment Structure Main Effects and Interactions Analysis of Variance for a Two-Factor Experiment Partial Factorial Structure Comparing Treatment Means - Are Multiple Comparison Methods Helpful? MORE ON FACTORIAL TREATMENT STRUCTURE More than Two Factors Factors with Two Levels The Double Benefit of Factorial Structure Many Factors and Small Blocks The Analysis of Confounded Experiments Split Plot Experiments Analysis of a Split Plot Experiment Experiments Repeated at Different Sites THE ASSUMPTIONS BEHIND THE ANALYSIS Our Assumptions Normality Variance Homogeneity Additivity Transformations of Data for Theoretical Reasons A More General Form of Analysis Empirical Detection of the Failure of Assumptions and Selection of Appropriate Transformations Practice and Presentation STUDYING LINEAR RELATIONSHIPS Linear Regression Assessing the Regression Line Inferences about the Slope of a Line Prediction Using a Regression Line Correlation Testing Whether the Regression is Linear Regression Analysis Using Computer Packages MORE COMPLEX RELATIONSHIPS Making the Crooked Straight Two Independent Variables Testing the Components of a Multiple Relationship Multiple Regression Possible Problems in Computer Multiple Regression LINEAR MODELS The Use of Models Models for Factors and Variables Comparison of Regressions Fitting Parallel Lines Covariance Analysis Regression in the Analysis of Treatment Variation NONLINEAR MODELS Advantages of Linear and Nonlinear Models Fitting Nonlinear Models to Data Inferences about Nonlinear Parameters Exponential Models Inverse Polynomial Models Logistic Models for Growth Curves THE ANALYSIS OF PROPORTIONS Data in the Form of Frequencies The 2 ' 2 Contingency Table More than Two Situations or More than Two Outcomes General Contingency Tables Estimation of Proportions Sample Sizes for Estimating Proportions MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA Models for Frequency Data Testing the Agreement of Frequency Data with Simple Models Investigating More Complex Models The Binomial Distribution The Poisson Distribution Generalized Models for Analyzing Experimental Data Log-Linear Models Logit Analysis of Response Data MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS Different Measurements on the Same Units Interdependence of Different Variables Repeated Measurements Joint (Bivariate) Analysis Indices of Combined Yield Investigating Relationships with Experimental Data ANALYZING AND SUMMARIZING MANY MEASUREMENTS Introduction to Multivariate Data Principal Component Analysis Covariance or Correlation Matrix Cluster Analysis Similarity and Dissimilarity Measures Hierarchical Clustering Comparison of PCA and Cluster Analysis CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN The Components of Design Units and Treatments Replication and Precision Different Levels of Variation and Within-Unit Replication Variance Components and Split Plot Designs Randomization Managing with Limited Resources Factors with Quantitative Levels Screening and Selection On-Farm Experiments SAMPLING FINITE POPULATIONS Experiments and Sample Surveys Simple Random Sampling Stratified Random Sampling Cluster Sampling, Multistage Sampling and Sampling Proportional to Size Ratio and Regression Estimates REFERENCES APPENDIX INDEX

Statistical Methods in Agriculture and Experimental Biology.

Soil treatments with Metarhizium brunneum EAMa 01/58-Su strain conducted in both Northern and Southern Spain reduced the olive fly (Bactrocera oleae) population density emerging from the soil during spring up to 70% in treated plots compared with controls. A model to determine the influence of rainfall on the conidial wash into different soil types was developed, with most of the conidia retained at the first 5 cm, regardless of soil type, with relative percentages of conidia recovered ranging between 56 and 95%. Furthermore, the possible effect of UV-B exposure time on the pathogenicity of this strain against B. oleae adults coming from surviving preimaginals and carrying conidia from the soil at adult emergence was also evaluated. The UV-B irradiance has no significant effect on M. brunneum EAMa 01/58-Su pathogenicity with B. oleae adult mortalities of 93, 90, 79, and 77% after 0, 2, 4, and 6 of UV-B irradiance exposure, respectively. In a next step for the use of these M. brunneum EAMa 01/58-Sun soil treatments within a B. oleae IPM strategy, its possible effect of on the B. oleae cosmopolitan parasitoid Psyttalia concolor, its compatibility with the herbicide oxyfluorfen 24% commonly used in olive orchards and the possible presence of the fungus in the olive oil resulting from olives previously placed in contact with the fungus were investigated. Only the highest conidial concentration (1 × 108 conidia ml-) caused significant P. concolor adult mortality (22%) with enduing mycosis in 13% of the cadavers. There were no fungal propagules in olive oil samples resulting from olives previously contaminated by EAMa 01/58-Su conidia. Finally, the strain was demonstrated to be compatible with herbicide since the soil application of the fungus reduced the B. oleae population density up to 50% even when it was mixed with the herbicide in the same tank. The fungal inoculum reached basal levels 4 months after treatments (1.6 × 103 conidia g soil-1). These results reveal both the efficacy and environmental and food safety of this B. oleae control method, protecting olive groves and improving olive oil quality without negative effects on the natural enemy P. concolor.

/pdf/metarhizium-brunneum-ascomycota-hypocreales-treatments-2v08io63nx.pdf

Metarhizium brunneum (Ascomycota; Hypocreales) Treatments Targeting Olive Fly in the Soil for Sustainable Crop Production.

Males and females of nearly all animals differ in their body size, a phenomenon called sexual size dimorphism (SSD). The degree and direction of SSD vary considerably among taxa, including among populations within species. A considerable amount of this variation is due to sex differences in body size plasticity. We examine how variation in these sex differences is generated by exploring sex differences in plasticity in growth rate and development time and the physiological regulation of these differences (e.g., sex differences in regulation by the endocrine system). We explore adaptive hypotheses proposed to explain sex differences in plasticity, including those that predict that plasticity will be lowest for traits under strong selection (adaptive canalization) or greatest for traits under strong directional selection (condition dependence), but few studies have tested these hypotheses. Studies that combine proximate and ultimate mechanisms offer great promise for understanding variation in SSD and sex differences in body size plasticity in insects.

/pdf/sex-differences-in-phenotypic-plasticity-affect-variation-in-45cjtz86dd.pdf

Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: from physiology to evolution.

Olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), is the major pest of commercial olives worldwide. Various aspects of its biology, ecology, management, and impact on olive production are highlighted. With the discovery of insecticidal resistance in some populations frequently treated with organophosphates, old and new control options are being investigated. The potential of biological control is examined. Surveys suggest that a small group of braconids in the Opiinae subfamily best represent the primary parasitoids attacking olive fruit fly in its native range. These species include Psyttalia lounsburyi, P. dacicida, P. concolor, P. ponerophaga, and Utetes africanus. Bracon celer, another braconid but in the Braconinae subfamily, is also reared from the fruit fly in its native range. The potential of these and other natural enemies is discussed with respect to olive fruit fly biology, commercial olive production, and biological constraints that may limit their success. We suggest that numerous species exist that should be further investigated as control agents for olive fruit fly in the many climatic regimes where the pest is found.

Olive Fruit Fly: Managing an Ancient Pest in Modern Times

Black soldier flies, Hermetia illucens (L.) (Diptera: Stratiomyidae), are of particular interest for their applications in waste management. Feeding on decaying organic waste, black soldier flies successfully reduce manure in confined animal feeding operations of poultry, swine, and cattle. To optimize waste conversion in confined animal feeding operations and landfill facilities, it is imperative to optimize black soldier fly development. Unfortunately, black soldier flies only convert waste during their larval feeding stages and therefore it is of interest to optimize the nonfeeding stages of development, specifically, the postfeeding and pupal stages. The time spent in these stages is thought to be determined by the pupation substrate encountered by the postfeeding larvae. The objective of this study was to determine the effect different pupation substrates have on postfeeding development time, pupation time, and adult emergence success. Five pupation substrates were compared: wood shavings, potting soil, topsoil, sand, and nothing. Postfeeding larvae took longer to reach pupation in the absence of a pupation substrate, although reaching pupation in the shortest time in potting soil and wood shavings. The time spent in the pupal stage was shortest in the absence of a pupation substrate. However, fewer adults emerged when a pupation substrate was not provided.

https://bioone.org/journals/Environmental-Entomology/volume-42/issue-2/EN12255/Substrate-Effects-on-Pupation-and-Adult-Emergence-of-Hermetia-illucens/10.1603/EN12255.pdf

Substrate Effects on Pupation and Adult Emergence of Hermetia illucens (Diptera: Stratiomyidae)

The spatial distribution of olive fruit flyBactrocera (Dacus) oleae (Gmelin) (Diptera: Tephritidae) field captures with McPhail traps within an experimental orchard was evaluated. Contour maps were constructed to examine the patterns in the 3-year trapping data. Captures varied widely inside the olive orchard, with traps suspended on wild olive trees exhibiting the poorest performance. Favorable microclimate, created by a standing water pool, appeared to be responsible for increased trap captures during the hot summer months. The positive role of the olive tree fruit load is also discussed.

The distribution of olive fruit fly captures with McPhail traps within an olive orchard

Wave height and setup in the sheltered area of a segmented, detached, rubble-mound, zero-freeboard breakwater on a steep beach

The influence of four abiotic factors (temperature, soil type, compaction, moisture) on the pupation depth of the wild Bactrocera (Dacus) oleae (Gmel.) larvae was studied using soils sampled in the field. Two temperatures (25 and 12°C), three different soil types (alluvial deposits, soil from decaying of limestone, soil from decaying of flysch), two compaction levels (low and high) and two moisture levels (10 and 50% field capacity) were tested in a factorial experiment with a total of 96 experimental units. Five larvae were placed on the soil surface of each test container and when burrowing was completed pupae were retrieved and pupation depth was recorded. The majority of larvae pupated in the top 3 cm and the mean depth of all units was 1.16 cm. The means differed significantly depending on soil type, moisture, the temperature–soil type interaction and the soil type–moisture interaction. Larvae pupated at a greater depth in limestone than in the other two soils. Depths were greater in soils at 50% field capacity than in those at 10% field capacity. In limestone and flysch the depth was greater at 25°C whereas no differences were found in alluvial soil. Different moisture levels had diverse effects in the three soil types; in alluvial soil and in flysch the increased moisture resulted in greater values but in limestone these were slightly lower. These results can be used in developing non chemical control measures and designing efficient sampling techniques for the insect in the ground.

Depth of pupation of the wild olive fruit fly, Bactrocera (Dacus) oleae (Gmel.) (Dipt., Tephritidae), as affected by soil abiotic factors

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The distribution of olive fruit fly captures with McPhail traps within an olive orchard

Wave height and setup in the sheltered area of a segmented, detached, rubble-mound, zero-freeboard breakwater on a steep beach

Depth of pupation of the wild olive fruit fly, Bactrocera (Dacus) oleae (Gmel.) (Dipt., Tephritidae), as affected by soil abiotic factors