Biology and quality assessment of Telenomus remus (Hymenoptera: Scelionidae) and Trichogramma spp. (Hymenoptera: Trichogrammatidae) in eggs of Spodoptera spp. for augmentative biological control programs.

The Spodoptera complex of the family Noctuidae, represented here by S. frugiperda (J.E. Smith), S. eridania (Stoll), S. albula (Walker), and S. cosmioides (Walker), is an important group of crop pests in Brazil. Spodoptera frugiperda and S. eridania are invasive in Africa, and the former also in Asia and Oceania. The egg parasitoids Telenomus remus Nixon (Hymenoptera: Scelionidae) and Trichogramma spp. (Hymenoptera: Trichogrammatidae) are potential control agents for field use against these noctuids. We evaluated the parasitism efficiency, development, and flight capacity of an isofemale line and a regular line of T. remus, and 2 genetically variable populations of Trichogramma pretiosum Riley and Trichogramma atopovirilia Oatman and Platner (Hymenoptera: Trichogrammatidae) in these 4 members of the Spodoptera complex. All parasitoids were able to develop in the 4 hosts. The parasitoids showed good flight capacity, except for the regular line of T. remus. The Trichogramma species, despite having high viability and female:male sex ratios, showed poorer parasitism performances than T. remus. The regular T. remus line also showed good parasitism capacity and high viability but had a predominance of males. In general, the isofemale line of T. remus showed good rates of parasitism and flight capacity as well as a high viability and sex ratio, proving to be a potential candidate for an augmentative biological-control program for Spodoptera spp Guenée (Lepidoptera: Noctuidae).

Biological basis for adoption of an isoline of Telenomus remus (Hymenoptera: Scelionidae) for an augmentative biological-control program for Spodoptera frugiperda (Lepidoptera: Noctuidae).

The widely distributed, polyphagous fall armyworm, Spodoptera frugiperda (J.E. Smith, 1797), is one of the most important crop pests worldwide. The egg-parasitoid wasp, Telenomus remus Nixon, 1937, is frequently described as a possible control agent for S. frugiperda. We selected an isoline of T. remus and evaluated its parasitism potential (for 24 h) in S. frugiperda eggs, in laboratory conditions, and also its ability to fly at different temperatures and relative humidity levels, aiming to provide basic information about this isoline. The selected isoline maintained good flight capacity without affecting its parasitism efficiency or developing inefficient haplotypes for biological-control programs, compared across generations to a regularline laboratory-reared for more than 60 generations. The flight capacity of the isoline was best at 25-30 °C and relative humidity 70-90%.

Intraguild predation influences oviposition behavior of blow flies (Diptera: Calliphoridae).

The objective of the present study was to determine whether blow flies (Diptera: Calliphoridae) are able to identify larvae of an intraguild predator species in the substrate and avoid laying eggs there. Blow flies oviposited in traps with different treatments: substrate only and substrate with larvae of Chrysomya albiceps (Wiedemann, 1819), Chrysomya megacephala (Fabricius, 1794), or Chrysomya putoria (Wiedemann, 1830). Ch. megacephala, Ch. putoria, and Lucilia eximia (Wiedemann, 1819) avoided laying eggs in the trap containing Ch. albiceps larvae. Cochliomyia macellaria (Fabricius, 1775) did not oviposit differently in each substrate but had overall low abundance. The prevalence of species on corpses may be influenced by the ability of the species to detect the presence of other species, mainly predators. In this sense, intraguild predation may result in misinterpretations of a crime scene and should be considered when assessing the minimum postmortem interval.

Quantifying intraclass correlations for count and time-to-event data.

The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.

Experimental power considerations--justifying replication for animal care and use committees.

A common practice in poultry science is to compare new treatments with a control or between treatments tested in planned experiments. The overall F-test from an ANOVA of the data allows the researcher to reject or not reject the null hypothesis. However, the correct conclusion from such analysis depends on sufficient replicates being included in the experiment. On the other hand, restrictions are imposed to reduce the number of birds used in experiments for welfare reasons and to save scarce resources. We review the basic concepts needed to determine the number of replicates before conducting an experiment. We use these concepts to assess the results of several real experiments and to show what might be done in future experiments. We describe how to do the computations in R software.

lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time.

BACKGROUND AND OBJECTIVE: Observational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples. METHODS: The longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy functions can be estimated based on fixed effects and variance components of the mixed-effects regression model. Inference is made through bootstrap confidence intervals and diagnostic can be done via plots, and statistical tests. RESULTS: The main features of the package are estimation and inference about the extent of agreement using numerical and graphical summaries. Moreover, our approach accommodates both balanced and unbalanced experimental designs or observational studies, and allows for different within-group error structures, while allowing for the inclusion of covariates in the linear predictor to control systematic variations in the response. All examples show that our methodology is flexible and can be applied to many different data types. CONCLUSIONS: The lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases.

A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data.

This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes, subject to censoring. Building upon work by Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010), gamma and normal random effects are included in a Weibull model, to account for overdispersion and between-subject effects, respectively. Unlike these authors, censoring is allowed for, and two estimation methods are presented. The partial marginalization approach to full maximum likelihood of Molenberghs et al. (2010) is contrasted with pseudo-likelihood estimation. A limited simulation study is conducted to examine the relative merits of these estimation methods. The modeling framework is employed to analyze data on recurrent asthma attacks in children on the one hand and on survival in cancer patients on the other.

An extended random-effects approach to modeling repeated, overdispersed count data.

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. The Poisson model for count data falls within this tradition. The family in general, and the Poisson model in particular, are at the same time convenient since mathematically elegant, but in need of extension since often somewhat restrictive. Two of the main rationales for existing extensions are (1) the occurrence of overdispersion, in the sense that the variability in the data is not adequately captured by the model's prescribed mean-variance link, and (2) the accommodation of data hierarchies owing to, for example, repeatedly measuring the outcome on the same subject, recording information from various members of the same family, etc. There is a variety of overdispersion models for count data, such as, for example, the negative-binomial model. Hierarchies are often accommodated through the inclusion of subject-specific, random effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these issues may occur simultaneously, models accommodating them at once are less than common. This paper proposes a generalized linear model, accommodating overdispersion and clustering through two separate sets of random effects, of gamma and normal type, respectively. This is in line with the proposal by Booth et al. (Stat Model 3:179-181, 2003). The model extends both classical overdispersion models for count data (Breslow, Appl Stat 33:38-44, 1984), in particular the negative binomial model, as well as the generalized linear mixed model (Breslow and Clayton, J Am Stat Assoc 88:9-25, 1993). Apart from model formulation, we briefly discuss several estimation options, and then settle for maximum likelihood estimation with both fully analytic integration as well as hybrid between analytic and numerical integration. The latter is implemented in the SAS procedure NLMIXED. The methodology is applied to data from a study in epileptic seizures.