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Leaf damage caused by herbivore feeding often triggers induced resistance in plants. However, some herbivores can take advantage of the resulting metabolic changes in host plants and may even manipulate plant resistance, leading to a phenomenon known as induced susceptibility. Previous work has shown that feeding by the whitefly Bemisia tabaci can reduce tomato Solanum lycopersicum resistance and that whiteflies tended to aggregate on infested plants. However, metabolomic changes in the plant and associated whitefly behavioural responses underlying this phenomenon remain poorly understood. We, therefore, investigated how B. tabaci infestation affects host physiology and the preference and performance of conspecific feeders. Bemisia tabaci adults exhibited consistent behavioural preferences for plants that experienced actual and simulated herbivory by conspecifics (consistent with observed effects on whitefly performance), but not for plants that were only mechanically wounded. Leaf volatiles and extracts of B. tabaci‐infested plants showed altered terpenoid and flavonoid profiles. Manipulative behavioural experiments indicated that suppression of the monoterpenes α‐phellandrene and α‐terpinene and of flavonoids by B. tabaci infestation influenced the foraging and oviposition preferences of conspecifics. These findings document key metabolic changes in plants exhibiting induced susceptibility and demonstrate their role in mediating herbivore foraging behaviour and aggregation on susceptible plants, thereby providing new insight into a relatively unexplored aspect of plant–herbivore interactions. A plain language summary is available for this article. Plain Language Summary

Societal Impact Statement Humans rely on plants in their environment for food and medicine. Understanding how humans select plant species will help us anticipate what plant species will be valuable for society in the future. However, previous approaches to study the drivers of plant selection have been criticized. We explored medicinal plant selection using a refined methodology and corroborate that humans select plant species for medicine in a non‐random way. Furthermore, we found that selection of specific plant organs across similar species is also non‐random. Improved understanding of medicinal plant selection can help identify new medicinal plants and the particular organs of plant species that are most likely to contain bioactive compounds to help improve human health and well‐being. Summary Ethnobotany, the science of human–plant interactions, has for long focused on documenting the traditional knowledge that humans have developed and accumulated over centuries toward plant uses. However, how such knowledge is constructed remains poorly understood and some of the methodological approaches developed for this purpose have been criticized. Here, we combine negative binomial models and phylogenetic comparative methods to test whether selection of medicinal plant species and organs by local people are non‐random using data from Benin, a country in West Africa with roughly 3,000 plant species. We found evidence for taxonomic and phylogenetic non‐random selection of medicinal plants: some taxa are preferentially used for medicine. Our analysis uncovers that plant organs are also non‐randomly selected for medicine. Beyond plant taxonomy, similar plant organs of closely related species tended to be used for similar treatments because these organs are likely to have similar secondary chemistry. Such non‐random organ selection was more apparent for certain plant organs such as roots, bark, and leaves, which are predicted to be more vital for population fitness and species persistence. Collectively, our study suggests that the emerging non‐random pattern of medicinal plant selection may be a consequence of heterogeneous within‐plant distribution of secondary chemistry across different organs proportionally to their importance to plant fitness as predicted by the optimal defense theory. Humans rely on plants in their environment for food and medicine. Understanding how humans select plant species will help us anticipate what plant species will be valuable for society in the future. However, previous approaches to study the drivers of plant selection have been criticized. We explored medicinal plant selection using a refined methodology and corroborate that humans select plant species for medicine in a non‐random way. Furthermore, we found that selection of specific plant organs across similar species is also non‐random. Improved understanding of medicinal plant selection can help identify new medicinal plants and the particular organs of plant species that are most likely to contain bioactive compounds to help improve human health and well‐being.

The objective was to evaluate aromatic plants’ effects on the acceptance, preference, egg load, and life span of females of the twospotted spider mite (TSSM) in laboratory experiments and TSSM population under aromatic plants’ intercropping in greenhouse experiments. The pseudofruits production was also evaluated. For the laboratory, basil’s, Chinese chives’, chives’, and garlic’s influence on TSSM were tested on strawberry leaves. Four laboratory experiments were conducted: (1) Multiple choice test; (2) T-shaped arena test; (3) host–plant acceptance on aromatic plant or strawberry leaves; and (4) performance of TSSMs on strawberry leaves under aromatic plant influence. For the greenhouse experiments, assessments of the TSSM populations were realized by observing TSSM with a 10× magnifying glass on strawberry leaves in a monocrop or intercropped with Chinese chives, chives, garlic, or onion. Pseudofruit production was evaluated. Our results show that strawberry leaves were strongly preferred by TSSM. The T-shaped arena test revealed that all aromatic plants repel the TSSM. The test with the performance of TSSM females revealed that aromatic plants affected the mite’s biological parameters. Chinese chives reduced the number of eggs laid per day by 33.22%, whereas garlic reduced the number by 17.30% and chives reduced it by 12.46%. The total number of eggs was reduced by 34.79% with Chinese chives and 25.65% with garlic. Greenhouse experiments showed that chives reduced TSSM populations on two cycles and Chinese chives and garlic reduced TSSM populations on the first cycle only. With our findings, we suggest that Chinese chives, chives, and garlic are the primary candidates for intercropping use against TSSM. Chinese chives and garlic reduced the total number of eggs, but only garlic reduced female mite longevity. However, none of the intercropping plants improved strawberry pseudofruit production.

Key messageFor genomic selection in clonally propagated crops with diploid (-like) meiotic behavior to be effective, crossing parents should be selected based on genomic predicted cross-performance unless dominance is negligible.For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously. Here, we compared different GS strategies for clonally propagated crops with diploid (-like) meiotic behavior, using strawberry as an example. We used stochastic simulation to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included (1) a breeding program that introduced GS in the first clonal stage, and (2) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were (1) parent selection based on genomic estimated breeding values (GEBVs) and (2) parent selection based on genomic predicted cross-performance (GPCP). Selection of parents based on GPCP produced faster genetic gain than selection of parents based on GEBVs because it reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using GPCP always produced the most genetic gain unless dominance was negligible. We conclude that (1) in clonal breeding programs with GS, parents should be selected based on GPCP, and (2) a two-part breeding program with parent selection based on GPCP to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

KEY MESSAGE : New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

Key messageMaximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50–55% (targeted) or 65–85% (untargeted) is needed to obtain 95% of the accuracy. With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs. The literature described many training set optimization methods, but there is a lack of a comprehensive comparison among them. This work aimed to provide an extensive benchmark among optimization methods and optimal training set size by testing a wide range of them in seven datasets, six different species, different genetic architectures, population structure, heritabilities, and with several GS models to provide some guidelines about their application in breeding programs. Our results showed that targeted optimization (uses information from the test set) performed better than untargeted (does not use test set data), especially when heritability was low. The mean coefficient of determination was the best targeted method, although it was computationally intensive. Minimizing the average relationship within the training set was the best strategy for untargeted optimization. Regarding the optimal training set size, maximum accuracy was obtained when the training set was the entire candidate set. Nevertheless, a 50–55% of the candidate set was enough to reach 95–100% of the maximum accuracy in the targeted scenario, while we needed a 65–85% for untargeted optimization. Our results also suggested that a diverse training set makes GS robust against population structure, while including clustering information was less effective. The choice of the GS model did not have a significant influence on the prediction accuracies.

Ethnobotany has evolved from a discipline that largely documented the diversity of plant use by local people to one focused on understanding how and why people select plants for a wide range of uses. This progress has been in response to a repeated call for theory-inspired and hypothesisdriven research to improve the rigor of the discipline. Despite improvements, recent ethnobotanical research has overemphasized the use of quantitative ethnobotany indices and statistical methods borrowed from ecology, yet underemphasized the development and integration of a strong theoretical foundation. To advance the field of ethnobotany as a hypothesis-driven, theoretically inspired discipline, it is important to first synthesize the existing theoretical lines of research. We review and discuss 17 major theories and hypotheses in ethnobotany that can be used as a starting point for developing research questions that advance our understanding of people-plant interactions. For each theory or major hypothesis, we identify its primary predictions and testable hypotheses and then discuss how these predictions have been tested. Developing research to test these predictions will make significant contributions to the field of ethnobotany and create the critical mass of primary literature necessary to develop meta-analyses and to advance new theories in ethnobotany.

Key message A replicated experiment on genomic selection in a maize landrace provides valuable insights on the design of rapid cycling recurrent pre-breeding schemes and the factors contributing to their success. The genetic diversity of landraces is currently underutilized for elite germplasm improvement. In this study, we investigated the potential of rapid cycling genomic selection for pre-breeding of a maize ( Zea mays L.) landrace population in replicated experiments. We trained the prediction model on a dataset (N = 899) composed of three landrace-derived doubled-haploid (DH) populations characterized for agronomic traits in 11 environments across Europe. All DH lines were genotyped with a 600 k SNP array. In two replications, three cycles of genomic selection and recombination were performed for line per se performance of early plant development, a major sustainability factor in maize production. From each cycle and replication, 100 DH lines were extracted. To evaluate selection response, the DH lines of all cycles and both replications (N = 688) were evaluated for per se performance of selected and unselected traits in seven environments. Selection was highly successful with an increase of about two standard deviations for traits under directional selection. Realized selection response was highest in the first cycle and diminished in following cycles. Selection gains predicted from genomic breeding values were only partially corroborated by realized gains estimated from adjusted means. Prediction accuracies declined sharply across cycles, but only for traits under directional selection. Retraining the prediction model with data from previous cycles improved prediction accuracies in cycles 2 and 3. Replications differed in selection response and particularly in accuracies. The experiment gives valuable insights with respect to the design of rapid cycling genomic selection schemes and the factors contributing to their success.

Key message Selection response in truncation selection across multiple sets of candidates hinges on their post-selection proportions, which can deviate grossly from their initial proportions. For BLUPs, using a uniform threshold for all candidates maximizes the selection response, irrespective of differences in population parameters. Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response ( Δ G Tot ) for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing Δ G Tot requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that Δ G Tot is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.

Key messageR/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy.Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance–covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise.