Prey are known to invest in costly antipredator behaviour when perceiving cues of dangerous, but not of relatively harmless predators. Whereas most studies investigate one type of antipredator behaviour, we studied several types (changes in oviposition, in escape and avoidance behaviour) in the spider mite Tetranychus evansi in response to cues from two predatory mites. The predator Phytoseiulus longipes is considered a dangerous predator for T. evansi, whereas Phytoseiulus macropilis has a low predation rate on this prey, thus is a much less dangerous predator. Spider mite females oviposited less on leaf disc halves with predator cues than on clean disc halves, independent of the predator species. On entire leaf discs, they laid fewer eggs in the presence of cues of the dangerous predator than on clean discs, but not in the presence of cues of the harmless predator. Furthermore, the spider mites escaped more often from discs with cues of the dangerous predator than from discs without predator cues, but they did not escape more from discs with cues of the harmless predator. The spider mites did not avoid plants with conspecifics and predators. We conclude that the spider mites displayed several different antipredator responses to the same predator species, and that some of these antipredator responses were stronger with cues of dangerous predators than with cues of harmless predators.

The evaluation of cultivars using multi-environment trials (MET) is an important step in plant breeding programs. One of the objectives of these evaluations is to understand the genotype by environment interaction (GEI). A method of determining the effect of GEI on the performance of cultivars is based on studies of adaptability and stability. Initial studies were based on linear regression; however, these methodologies have limitations, mainly in trials with genetic or statistical unbalanced, heterogeneity of residual variances, and genetic covariance. An alternative would be the use of random regression models (RRM), in which the behavior of the genotypes is characterized as a reaction norm using longitudinal data or repeated measurements and information regarding a covariance function. The objective of this work was the application of RRM in the study of the behavior of common bean cultivars using a MET, based on Legendre polynomials and genotype-ideotype distances. We used a set of 13 trials, which were classified as unfavorable or favorable environments. The results revealed that RRM enables the prediction of the genotypic values of cultivars in environments where they were not evaluated with high accuracy values, thereby circumventing the unbalanced of the experiments. From these values, it was possible to measure the genotypic adaptability according to ideotypes, according to their reaction norms. In addition, the stability of the cultivars can be interpreted as variation in the behavior of the ideotype. The use of ideotypes based on real data allowed a better comparison of the performance of cultivars across environments. The use of RRM in plant breeding is a good alternative to understand the behavior of cultivars in a MET, especially when we want to quantify the adaptability and stability of genotypes.

Herbivores select host plants depending on plant quality and the presence of predators and competitors. Competing herbivores change host plant quantity through consumption, but they can also change plant quality through induction of plant defences, and this affects the performance of herbivores that arrive later on the plant. Some herbivores, such as the spider mite Tetranychus evansi, do not induce, but suppress plant defences, and later-arriving herbivores can profit from this suppression. It has been suggested that the dense web produced by this spider mite serves to prevent other herbivores to settle on the plant and benefit from the suppressed defences. Here, we confirmed this by studying the preference and performance of the whitefly Bemisia tabaci, a generalist herbivorous pest. To disentangle the effects through changes in plant defences from the effects of spider-mite web, we included treatments with a strain of the closely-related web-producing spider mite T. urticae, which induces plant defences. Whiteflies did perform worse on plants with defences induced by T. urticae, but, in contrast to other herbivores, did not perform better on plants with defences suppressed by T. evansi. Moreover, the web of both spider mites reduced the juvenile survival of whiteflies, and whiteflies avoided plants that were covered with web. Hence, whitefly performance was not only affected by plant quality and induced plant defences, but also through the web produced by spider mites, which thus serves to protect against potential competitors, especially when these could profit from the suppression of plant defences by the mites.

Dataset used at work 'Adaptability and stability of plants using random regression models' by Michel Henriques de Souza; José Domingos Pereira Júnior; Skarlet De Marco Steckling; Jussara Mencalha; Fabíola dos Santos Dias; João Romero do Amaral Santos de Carvalho Rocha; Pedro Crescêncio Souza Carneiro; José Eustáquio de Souza Carneiro.

The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method.