Information arms race explains plant-herbivore chemical communication in ecological communities

Induced resistance to herbivory and the intelligent plant

Plant induced responses to environmental stressors are increasingly studied in a behavioral ecology context. This is particularly true for plant induced responses to herbivory that mediate direct and indirect defenses, and tolerance. These seemingly adaptive alterations of plant defense phenotypes in the context of other environmental conditions have led to the discussion of such responses as intelligent behavior. Here we consider the concept of plant intelligence and some of its predictions for chemical information transfer in plant interaction with other organisms. Within this framework, the flow, perception, integration, and storage of environmental information are considered tunable dials that allow plants to respond adaptively to attacking herbivores while integrating past experiences and environmental cues that are predictive of future conditions. The predictive value of environmental information and the costs of acting on false information are important drivers of the evolution of plant responses to herbivory. We identify integrative priming of defense responses as a mechanism that allows plants to mitigate potential costs associated with acting on false information. The priming mechanisms provide short- and long-term memory that facilitates the integration of environmental cues without imposing significant costs. Finally, we discuss the ecological and evolutionary prediction of the plant intelligence hypothesis.

WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.

Host Volatiles Potentially Drive Two Evolutionarily Related Weevils to Select Different Grains

The Sitophilus zeamais (maize weevil) and Sitophilus oryzae (rice weevil) are two insect pests that have caused huge economic losses to stored grains worldwide. It is urgent to develop an environmentally friendly strategy for the control of these destructive pests. Here, the olfactory-mediated selection preference of the two weevil species to three stored grains was analyzed, which should help establish a pull-push system in managing them. Bioassays showed that maize weevil adults prefer to select maize, followed by paddy and wheat, while rice weevil adults mainly migrate towards wheat. Volatile analyses revealed that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene are the major components in volatiles from both maize and wheat, but the abundance of these chemicals is much lower in maize than that in wheat. The volatile limonene was only detected in paddy. Y-tube bioassays suggest that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene were all attractive to both weevils, whereas limonene was attractive only to rice weevils. Overall, maize weevil appeared more sensitive to the tested volatiles based on having much lower effective concentrations of these volatiles needed to attract them. The differences in volatile profiles among the grains and the sensitivity of the two species towards these volatiles may explain the behavioral differences between maize and rice weevils in selecting host grains. The differences in sensitivity of maize and rice weevils towards host volatile components with abundance differences are likely determinants driving the two insect species to migrate towards different host grains.

High-resolution kinetics of herbivore-induced plant volatile transfer reveal clocked response patterns in neighboring plants

Volatiles emitted by herbivore-attacked plants (senders) can enhance defenses in neighboring plants (receivers), however, the temporal dynamics of this phenomenon remain poorly studied. Using a custom-built, high-throughput proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) system, we explored temporal patterns of volatile transfer and responses between herbivore-attacked and undamaged maize plants. We found that continuous exposure to natural blends of herbivore-induced volatiles results in clocked temporal response patterns in neighboring plants, characterized by an induced terpene burst at the onset of the second day of exposure. This delayed burst is not explained by terpene accumulation during the night, but coincides with delayed jasmonate accumulation in receiver plants. The delayed burst occurs independent of day:night light transitions and cannot be fully explained by sender volatile dynamics. Instead, it is the result of a stress memory from volatile exposure during the first day and secondary exposure to bioactive volatiles on the second day. Our study reveals that prolonged exposure to natural blends of stress-induced volatiles results in a response that integrates priming and direct induction into a distinct and predictable temporal response pattern. This provides an answer to the long-standing question of whether stress volatiles predominantly induce or prime plant defenses in neighboring plants, by revealing that they can do both in sequence.

Plant volatiles mediated the orientation preference of slugs to different plant species

BACKGROUND

Slugs mechanically damage plant leaves, resulting in significant economic losses. However, there are limited cost-efficient strategies available in slug management. By studying how slugs utilize plant volatiles to locate host plants, we can gain insights into the design of attractants and repellents.

RESULTS

Bioassay results suggest slugs (Agriolimax agrestis) prefer to orientate to lettuce (Lactuca sativa), cabbage (Brassica oleracea L.), and young tobacco seedlings, compared with old tobacco seedlings. We analyzed the volatomics of lettuce, cabbage, young and old tobacco seedlings. 2-(2-butoxyethoxy)-ethanol acetate (2EA) had high abundance while nonanal, decanal, and β-cylocitral had relatively low content in volatiles. Old tobacco seedlings released significantly more hexanal but fewer 1,4-dihydro-4-oxopyridazine (DO). In olfactory tests, hexanal, nonanal, decanal, and β-cylocitral showed strong repellency to slugs, while DO at a dose of 500 ng/μL and 2EA at a dose of 1% were attractive to slugs. The two alkanes, hexadecane and heptadecane, had no effect on slug orientating to host plants. DO and 2EA can thus alleviate the repellency of hexanal, nonanal, decanal and β-cylocitral.

CONCLUSION

The high emission of hexanal in old tobacco seedlings helps repel slugs, while 2EA and DO attract slugs to lettuce and cabbage. These findings suggest that these chemicals can be utilized in the design of repellents and attractants, and contribute to constructing a push-pull system for slug control. © 2023 Society of Chemical Industry.

Decreased metabolic diversity in common beans associated with domestication revealed by untargeted metabolomics, information theory, and molecular networking

The process of crop domestication leads to a dramatic reduction in the gene expression associated with metabolic diversity. Genes involved in specialized metabolism appear to be particularly affected. Although there is ample evidence of these effects at the genetic level, a reduction in diversity at the metabolite level has been taken for granted despite having never been adequately accessed and quantified. Here we leveraged the high coverage of ultra high performance liquid chromatography-high-resolution mass spectrometry based metabolomics to investigate the metabolic diversity in the common bean (Phaseolus vulgaris). Information theory highlights a shift towards lower metabolic diversity and specialization when comparing wild and domesticated bean accessions. Moreover, molecular networking approaches facilitated a broader metabolite annotation than achieved to date, and its integration with gene expression data uncovers a metabolic shift from specialized metabolism towards central metabolism upon domestication of this crop.

Plant volatiles and priority effects interactively determined initial community assembly of arthropods on multiple willow species

Plant traits, which are often species specific, can serve as environmental filtering for community assembly on plants. At the same time, the species identity of the initially colonizing arthropods would vary between plant individuals, which would subsequently influence colonizing arthropods and community development in the later stages. However, it remains unclear whether interindividual divergence due to priority effects is equally important as plant trait-specific environmental filtering in the initial stages. In this study, we propose that plant volatile organic compounds (PVOCs) may play a crucial role as an environmental filter in the initial stages of community assembly, which can prevent the community assembly process from being purely stochastic. To test this hypothesis, we conducted short term but highly frequent monitoring (19 observations over 9 days) of arthropod community assembly on intact individuals of six willow species in a common garden. PVOC compositions were analyzed before starting the experiment and compared with arthropod compositions occurring on Days 1-2 of the experiment (earliest colonizer community) and those occurring on Days 8-9 of the experiment (subsequent colonizer community). Unintentionally, deer herbivory also occurred at night of Day 2. Distance-based statistics demonstrated that PVOC compositions were plant species specific, but neither the earliest colonizer nor the subsequent colonizer community composition could be explained by plant species identity. Rather, Procrustes analysis showed that both the PVOC composition and that of the earliest colonizer community can be used to explain the subsequent colonizer community. In addition, the linkage between PVOCs and the subsequent colonizer community was stronger on individuals with deer herbivory. These findings indicate that PVOCs have widespread effects on initial community assembly, as well as priority effects brought on by stochastic immigration, and that plant species identity only has weak and indirect effects on the actual composition of the community.

Volatile-mediated plant-plant communication and higher-level ecological dynamics

Volatile organic compounds (VOCs) in general and herbivory-induced plant volatiles (HIPVs) in particular are increasingly understood as major mediators of information transfer between plant tissues. Recent findings have moved the field of plant communication closer to a detailed understanding of how plants emit and perceive VOCs and seem to converge on a model that juxtaposes perception and emission mechanisms. These new mechanistic insights help to explain how plants can integrate different types of information and how environmental noise can affect the transmission of information. At the same time, ever-new functions of VOC-mediated plant-plant interactions are being revealed. Chemical information transfer between plants is now known to fundamentally affect plant organismal interactions and, additionally, population, community, and ecosystem dynamics. One of the most exciting new developments places plant-plant interactions along a behavioral continuum with an eavesdropping strategy at one end and mutually beneficial information-sharing among plants within a population at the other. Most importantly and based on recent findings as well as theoretical models, plant populations can be predicted to evolve different communication strategies depending on their interaction environment. We use recent studies from ecological model systems to illustrate this context dependency of plant communication. Moreover, we review recent key findings about the mechanisms and functions of HIPV-mediated information transfer and suggest conceptual links, such as to information theory and behavioral game theory, as valuable tools for a deeper understanding of how plant-plant communication affects ecological and evolutionary dynamics.

Leaf volatile and nonvolatile metabolites show different levels of specificity in response to herbivory

Plants produce diverse chemical defenses with contrasting effects on different insect herbivores. Deploying herbivore-specific responses can help plants increase their defensive efficiency. Here, we explore how variation in induced plant responses correlates with herbivore species, order, feeding guild, and level of specialization. In a greenhouse experiment, we exposed 149 plants of Salix fragilis (Linnaeus, 1753) to 22 herbivore species naturally associated with this host. The insects belonged to four orders (Coleoptera, Lepidoptera, Hemiptera, and Hymenoptera), three feeding guilds (external leaf-chewers, leaf-tying chewers, and sap-sucking), and included both dietary specialists and generalists. Following herbivory, we quantified induced changes in volatiles and nonvolatile leaf metabolites. We performed multivariate analyses to assess the correlation between herbivore order, feeding guild, dietary specialization, chewing damage by herbivores, and induced responses. The volatile composition was best explained by chewing damage and insect order, with Coleoptera and Lepidoptera eliciting significantly different responses. Furthermore, we recorded significant differences in elicited volatiles among some species within the two orders. Variation in nonvolatile leaf metabolites was mainly explained by the presence of insects, as plants exposed to herbivores showed significantly different metabolites from controls. Herbivore order also played a role to some extent, with beetles eliciting different responses than other herbivores. The induction of volatile and nonvolatile leaf metabolites shows different levels of specificity. The specificity in volatiles could potentially serve as an important cue to specialized predators or parasitoids, increasing the efficacy of volatiles as indirect defenses. By contrast, the induction of nonvolatile leaf metabolites was largely unaffected by herbivore identity. Most nonvolatile metabolites were downregulated, possibly indicating that plants redirected their resources from leaves in response to herbivory. Our results demonstrate how diverse responses to herbivores can contribute to the diversity of plant defensive strategies.

Information theory and machine learning illuminate large-scale metabolomic responses of Brachypodium distachyon to environmental change

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ-condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)-based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML-based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co-accumulation analysis further identified condition-specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio-Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.

Dynamic environmental interactions shaped by vegetative plant volatiles

Covering: up to November 2022Plants shape terrestrial ecosystems through physical and chemical interactions. Plant-derived volatile organic compounds in particular influence the behavior and performance of other organisms. In this review, we discuss how vegetative plant volatiles derived from leaves, stems and roots are produced and released into the environment, how their production and release is modified by abiotic and biotic factors, and how they influence other organisms. Vegetative plant volatiles are derived from different biosynthesis and degradation pathways and are released via distinct routes. Both biosynthesis and release are regulated by other organisms as well as abiotic factors. In turn, vegetative plant volatiles modify the physiology and the behavior of a wide range of organisms, from microbes to mammals. Several concepts and frameworks can help to explain and predict the evolution and ecology of vegetative plant volatile emission patterns of specific pathways: multifunctionality of specialized metabolites, chemical communication displays and the information arms race, and volatile physiochemistry. We discuss how these frameworks can be leveraged to understand the evolution and expression patterns of vegetative plant volatiles. The multifaceted roles of vegetative plant volatiles provide fertile grounds to understand ecosystem dynamics and harness their power for sustainable agriculture.

Quantifying chemodiversity considering biochemical and structural properties of compounds with the R package chemodiv

Plants produce large numbers of phytochemical compounds affecting plant physiology and interactions with their biotic and abiotic environment. Recently, chemodiversity has attracted considerable attention as an ecologically and evolutionary meaningful way to characterize the phenotype of a mixture of phytochemical compounds. Currently used measures of phytochemical diversity, and related measures of phytochemical dissimilarity, generally do not take structural or biosynthetic properties of compounds into account. Such properties can be indicative of the compounds' function and inform about their biosynthetic (in)dependence, and should therefore be included in calculations of these measures. We introduce the R package chemodiv, which retrieves biochemical and structural properties of compounds from databases and provides functions for calculating and visualizing chemical diversity and dissimilarity for phytochemicals and other types of compounds. Our package enables calculations of diversity that takes the richness, relative abundance and - most importantly - structural and/or biosynthetic dissimilarity of compounds into account. We illustrate the use of the package with examples on simulated and real datasets. By providing the R package chemodiv for quantifying multiple aspects of chemodiversity, we hope to facilitate investigations of how chemodiversity varies across levels of biological organization, and its importance for the ecology and evolution of plants and other organisms.

Advances in Receptor-like Protein Kinases in Balancing Plant Growth and Stress Responses

Accompanying the process of growth and development, plants are exposed to ever-changing environments, which consequently trigger abiotic or biotic stress responses. The large protein family known as receptor-like protein kinases (RLKs) is involved in the regulation of plant growth and development, as well as in the response to various stresses. Understanding the biological function and molecular mechanism of RLKs is helpful for crop breeding. Research on the role and mechanism of RLKs has recently received considerable attention regarding the balance between plant growth and environmental adaptability. In this paper, we systematically review the classification of RLKs, the regulatory roles of RLKs in plant development (meristem activity, leaf morphology and reproduction) and in stress responses (disease resistance and environmental adaptation). This review focuses on recent findings revealing that RLKs simultaneously regulate plant growth and stress adaptation, which may pave the way for the better understanding of their function in crop improvement. Although the exact crosstalk between growth constraint and plant adaptation remains elusive, a profound study on the adaptive mechanisms for decoupling the developmental processes would be a promising direction for the future research.

Symbiotic microbiota and odor ensure mating in time for giant pandas

To achieve reproduction, male solitary mammals need to locate females using chemical communication with high levels of precision. In the case of giant pandas, the total estrus period of females was usually 15 days each year, however, successful mating activity is finished within 3 days from respective home range. The mating pattern of giant pandas, where multiple males compete for each female requires females employ efficient systems to communicate their estrus phases. To verifying whether the scent secretions of giant pandas changes by gender and estrus progression, the microbiota and compounds in 29 anogenital gland samples from 14 individuals during estrus were analyzed by 16S rRNA sequencing and GC-MS. We show that the microbiota communities covary by gender with 4 particular compounds of scent secretions. Among 597 genera, 34 were identified as biomarkers that could be used to distinguish between different estrus phases. By bacterial-compounds co-analysis, 3 fatty ester acids and squalene compounds covaried with the development of estrus in the bacterial communities of female giant pandas. This study helps clarify how a large, solitary mammal expresses accurate information to improve the likelihood of successful reproduction by changing the composition of microbiota and odor compounds of anogenital glands during estrus.

A new conceptual and quantitative approach to exploring and defining potential open-access olfactory information

All organisms emit odour, providing 'open-access' olfactory information for any receiver with the right sensory apparatus. Characterizing open-access information emitted by groups of organisms, such as plant species, provides the means to answer significant questions about ecological interactions and their evolution. We present a new conceptual framework defining information reliability and a practical method to characterize and recover information from amongst olfactory noise. We quantified odour emissions from two tree species, one focal group and one outgroup, to demonstrate our approach using two new R statistical functions. We explore the consequences of relaxing or tightening criteria defining information and, from thousands of odour combinations, we identify and quantify those few likely to be informative. Our method uses core general principles characterizing information while incorporating knowledge of how receivers detect and discriminate odours. We can now map information in consistency-precision reliability space, explore the concept of information, and test information-noise boundaries, and between cues and signals.

Towards a system-level causative knowledge of pollinator communities

Pollination plays a central role in both crop production and maintaining biodiversity. However, habitat loss, pesticides, invasive species and larger environmental fluctuations are contributing to a dramatic decline of pollinators worldwide. Different management solutions require knowledge of how ecological communities will respond following interventions. Yet, anticipating the response of these systems to interventions remains extremely challenging due to the unpredictable nature of ecological communities, whose nonlinear behaviour depends on the specific details of species interactions and the various unknown or unmeasured confounding factors. Here, we propose that this knowledge can be derived by following a probabilistic systems analysis rooted on non-parametric causal inference. The main outcome of this analysis is to estimate the extent to which a hypothesized cause can increase or decrease the probability that a given effect happens without making assumptions about the form of the cause-effect relationship. We discuss a road map for how this analysis can be accomplished with the aim of increasing our system-level causative knowledge of natural communities. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.

Changes in primary metabolites and volatile organic compounds in cotton seedling leaves exposed to silver ions and silver nanoparticles revealed by metabolomic analysis

In the area of climate change, nanotechnology provides handy tools for improving crop production and assuring sustainability in global agricultural system. Due to excellent physiological and biochemical properties, silver nanoparticles (AgNPs) have been widely studied for potential use in agriculture. However, there are concerns about the mechanism of the toxic effects of the accumulation of AgNPs on crop growth and development. In this study, the impacts of AgNPs on cotton (Gossypium hirsutum) seedlings were evaluated by integrating physiological and comprehensive metabolomic analyses. Potting-soil-grown, two-week-old cotton seedlings were foliar-exposed to 5 mg/plant AgNP or 0.02 mg/plant Ag⁺ (equivalent to the free Ag⁺ released from AgNPs). Primary metabolites and volatile organic compounds (VOCs) were identified by gas chromatography-mass spectrometry (GC-MS) and solid-phase microextraction (SPME) GC-MS, respectively. AgNPs inhibited the photosynthetic capacity of the cotton leaves. The metabolic spectrum analysis identified and quantified 73 primary metabolites and 45 VOCs in cotton leaves. Both treatments significantly changed the metabolite profiles of plant leaves. Among the primary metabolites, AgNPs induced marked changes in amino acids, sugars and sugar alcohols. Among the VOCs, 13 volatiles, mainly aldehydes, alkanes and terpenoids, were specifically altered only in response to AgNPs. In summary, our study showed that the comprehensive influence of AgNPs on primary metabolites and VOCs was not merely attributed to the released Ag⁺ but was caused by AgNP-specific effects on cotton leaves. These results provide important knowledge about the physiological and chemical changes in cotton leaves upon exposure to AgNPs and offer a new insight for supporting the sustainable use of AgNPs in agriculture.

Molecular ecology of plant volatiles in interactions with insect herbivores

Plant-derived volatile organic compounds (VOCs) play pivotal roles in interactions with insect herbivores. Individual VOCs can be directly toxic or deterrent, serve as signal molecules to attract natural enemies, and/or be perceived by distal plant tissues as a priming signal to prepare for expected herbivory. Environmental conditions, as well as the specific plant-insect interaction being investigated, strongly influence the observed functions of VOC blends. The complexity of plant-insect chemical communication via VOCs is further enriched by the sophisticated molecular perception mechanisms of insects, which can respond to one or more VOCs and thereby influence insect behavior in a manner that has yet to be fully elucidated. Despite numerous gaps in the current understanding of VOC-mediated plant-insect interactions, successful pest management strategies such as push-pull systems, synthetic odorant traps, and crop cultivars with modified VOC profiles have been developed to supplement chemical pesticide applications and enable more sustainable agricultural practices. Future studies in this field would benefit from examining the responses of both plants and insects in the same experiment to gain a more complete view of these interactive systems. Furthermore, a molecular evolutionary study of key genetic elements of the ecological interaction phenotypes could provide new insights into VOC-mediated plant communication with insect herbivores.

Development of a widely targeted volatilomics method for profiling volatilomes in plants

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a "targeted spectra extraction" algorithm to address spectral convolution, constructing a high-coverage MS² spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

Cooperative herbivory between two important pests of rice

Normally, when different species of herbivorous arthropods feed on the same plant this leads to fitness-reducing competition. We found this to be different for two of Asia's most destructive rice pests, the brown planthopper and the rice striped stem borer. Both insects directly and indirectly benefit from jointly attacking the same host plant. Double infestation improved host plant quality, particularly for the stemborer because the planthopper fully suppresses caterpillar-induced production of proteinase inhibitors. It also reduced the risk of egg parasitism, due to diminished parasitoid attraction. Females of both pests have adapted their oviposition behaviour accordingly. Their strong preference for plants infested by the other species even overrides their avoidance of plants already attacked by conspecifics. This cooperation between herbivores is telling of adaptations resulting from the evolution of plant-insect interactions, and points out mechanistic vulnerabilities that can be targeted to control these major pests.

Untangling the complexity of priority effects in multispecies communities

The history of species immigration can dictate how species interact in local communities, thereby causing historical contingency in community assembly. Since immigration history is rarely known, these historical influences, or priority effects, pose a major challenge in predicting community assembly. Here, we provide a graph-based, non-parametric, theoretical framework for understanding the predictability of community assembly as affected by priority effects. To develop this framework, we first show that the diversity of possible priority effects increases super-exponentially with the number of species. We then point out that, despite this diversity, the consequences of priority effects for multispecies communities can be classified into four basic types, each of which reduces community predictability: alternative stable states, alternative transient paths, compositional cycles and the lack of escapes from compositional cycles to stable states. Using a neural network, we show that this classification of priority effects enables accurate explanation of community predictability, particularly when each species immigrates repeatedly. We also demonstrate the empirical utility of our theoretical framework by applying it to two experimentally derived assembly graphs of algal and ciliate communities. Based on these analyses, we discuss how the framework proposed here can help guide experimental investigation of the predictability of history-dependent community assembly.

Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity

The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.

Predictability of Biotic Stress Structures Plant Defence Evolution

To achieve ecological and reproductive success, plants need to mitigate a multitude of stressors. The stressors encountered by plants are highly dynamic but typically vary predictably due to seasonality or correlations among stressors. As plants face physiological and ecological constraints in responses to stress, it can be beneficial for plants to evolve the ability to incorporate predictable patterns of stress in their life histories. Here, we discuss how plants predict adverse conditions, which plant strategies integrate predictability of biotic stress, and how such strategies can evolve. We propose that plants commonly optimise responses to correlated sequences or combinations of herbivores and pathogens, and that the predictability of these patterns is a key factor governing plant strategies in dynamic environments.

Plant volatiles mediate evolutionary interactions between plants and tephritid flies and are evolutionarily more labile than non-volatile defenses

Studies show that plant defenses influence the host-use of herbivores and tend to be evolutionarily more labile than herbivore traits (e.g. feeding preferences). However, all previous studies have focused exclusively on non-volatile plant defenses thereby overlooking the roles of plant volatiles. We hypothesized that volatiles are equally important determinants of herbivore host-use and are evolutionarily more labile than herbivore traits. To test these hypotheses, the following experiments were conducted. We identified the volatiles and non-volatiles of 17 Asteraceae species and measured their relative contents. We also used a highly resolved bipartite trophic network of the 17 host species and 20 herbivorous (pre-dispersal seed predator) tephritid fly species to determine the evolutionary interactions between plants and herbivores. The chemical data showed that interspecific similarity in volatiles-but not non-volatiles and phylogenetic distance-significantly accounted for the herbivore community across the plant species; this implies that plant volatiles-but not non-volatile compounds and species identity-dictate plant-tephritid fly interactions. Moreover, we observed phylogenetic signal for non-volatiles but not for volatiles; therefore closely related herbivores do not necessarily use closely related host species with similar non-volatiles, but do tend to attack plants producing similar volatiles. Thus, plant volatiles are evolutionarily more labile than non-volatiles and herbivore traits associate with host use. These results show that the interactions between plants and herbivores are evolutionary asymmetric, shed light on the role of plant volatiles in plant-herbivore interactions, and highlight the need to include data for both volatiles and non-volatiles when investigating plant-animal interactions.

The Structure of Ecological Networks Across Levels of Organization

Interactions connect the units of ecological systems, forming networks. Individual-based networks characterize variation in niches among individuals within populations. These individual-based networks merge with each other, forming species-based networks and food webs that describe the architecture of ecological communities. Networks at broader spatiotemporal scales portray the structure of ecological interactions across landscapes and over macroevolutionary time. Here, I review the patterns observed in ecological networks across multiple levels of biological organization. A fundamental challenge is to understand the amount of interdependence as we move from individual-based networks to species-based networks and beyond. Despite the uneven distribution of studies, regularities in network structure emerge across scales due to the fundamental architectural patterns shared by complex networks and the interplay between traits and numerical effects. I illustrate the integration of these organizational scales by exploring the consequences of the emergence of highly connected species for network structures across scales.

Quantum-Like Interdependence Theory Advances Autonomous Human-Machine Teams (A-HMTs)

As humanity grapples with the concept of autonomy for human–machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.

Converting Ecological Currencies: Energy, Material, and Information Flows

Understanding how the three currencies of life - energy, material, and information - interact is a key step towards synthesis in ecology and evolution. However, current theory focuses on the role of matter as a resource and energy, and typically ignores how the same matter can have other important effects as a carrier of information or modifier of the environment. Here we present the hypothesis that the dynamic conversion of matter by organisms among its three currencies mediates the structure and function of ecosystems, and that these effects can even supersede the effects of matter as a resource. Humans are changing the information in the environment and this is altering species interactions and flows of matter within and among ecosystems.

Hiding in plain smell

A common rice pest can avoid its natural parasite by settling on plants that smell like they have been damaged by a species of caterpillar.