Using DNA metabarcoding to decipher the diet plant component of mammals from the Eastern Mediterranean region

Longevity of species populations depends largely on interactions among animals and plants in an ecosystem. Predation and seed dispersal are among the most important interactions necessary for species conservation and persistence, and diet analysis is a prerequisite tool to evaluate these interactions. Understanding these processes is crucial for identifying conservation targets and for executing efficient reforestation and ecological restoration. In this study, we applied a scat DNA metabarcoding technique using the P6-loop of the trnL (UAA) chloroplastic marker to describe the seasonal plant diet composition of 15 mammal species from a highly biodiverse Lebanese forest in the Eastern Mediterranean. We also recovered plant seeds, when present, from the scats for identification. The mammal species belong to 10 families from 5 different orders. More than 133 plant species from 54 plant families were detected and identified. Species from the Rosaceae, Poaceae, Apiaceae, Fabaceae, Fagaceae and Berberidaceae families were consumed by the majority of the mammals and should be taken into consideration in future reforestation and conservation projects. Our results showed that the DNA metabarcoding approach provides a promising method for tracking the dietary plant components of a wide diversity of mammals, yielding key insights into plant-animal interactions inside Lebanon’s forests.

From Diversity to Complexity: Microbial Networks in Soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, an definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research.

Structure-based identification of sensor species for anticipating critical transitions

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure—that is, the network topology of plant–animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of “sensor species,” whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant–pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

Attracting pollinators vs escaping herbivores: eco-evolutionary dynamics of plants confronted with an ecological trade-off

A large number of plant traits are subject to an ecological trade-off between attracting pollinators and escaping herbivores. The interplay of both plant-animal interactions determines their evolution. Within a plant-pollinator-herbivore community in which interaction strengths depend on trait-matching, eco-evolutionary dynamics are studied using the framework of adaptive dynamics. We characterize the type of selection acting on the plant phenotype and the consequences for multispecies coexistence. We find that pollination favors stabilizing selection and coexistence. In contrast, herbivory fosters runaway selection, which threatens plant-animal coexistence. These contrasting dynamics highlight the key role of ecological trade-offs in structuring ecological communities. In particular, we show that disruptive selection is possible when such trade-offs are strong. While the interplay of pollination and herbivory is known to maintain plant polymorphism in several cases, our work suggests that it might also have fueled the diversification process itself.

Cascading effects of caffeine intake by primary consumers to the upper trophic level

Secondary metabolites are central to understanding the evolution of plant–animal interactions. Direct effects on phytophagous animals are well-known, but how secondary consumers adjust their behavioural and physiological responses to the herbivore's diet remains more scarcely explored for some metabolites. Caffeine is a neuroactive compound that affects both the behaviour and physiology of several animal species, from humans to insects. It is an alkaloid present in nectar, leaves and even sap of numerous species of plants where it plays a role in chemical defences against herbivores and pathogens. Caffeine effects have been overlooked in generalist herbivores that are not specialized in coffee or tea plants. Using a host–parasitoid system, we show that caffeine intake at a relatively low dose affects longevity and fecundity of the primary consumer, but also indirectly of the secondary one, suggesting that this alkaloid and/or its effects can be transmitted through trophic levels and persist in the food chain. Parasitism success was lowered by ≈16% on hosts fed with caffeine, and parasitoids of the next generation that have developed in hosts fed on caffeine showed a reduced longevity, but no differences in mass and size were found. This study helps at better understanding how plant secondary metabolites, such as caffeine involved in plant–animal interactions, could affect primary consumers, could have knock-on effects on upper trophic levels over generations, and could modify interspecific interactions in multitrophic systems.

Comparative Metabolomics of Fruits and Leaves in a Hyperdiverse Lineage Suggests Fruits Are a Key Incubator of Phytochemical Diversification

Interactions between plants and leaf herbivores have long been implicated as the major driver of plant secondary metabolite diversity. However, other plant-animal interactions, such as those between fruits and frugivores, may also be involved in phytochemical diversification. Using 12 species of Piper, we conducted untargeted metabolomics and molecular networking with extracts of fruits and leaves. We evaluated organ-specific secondary metabolite composition and compared multiple dimensions of phytochemical diversity across organs, including richness, structural complexity, and variability across samples at multiple scales within and across species. Plant organ identity, species identity, and the interaction between the two all significantly influenced secondary metabolite composition. Leaves and fruit shared a majority of compounds, but fruits contained more unique compounds and had higher total estimated chemical richness. While the relative levels of chemical richness and structural complexity across organs varied substantially across species, fruit diversity exceeded leaf diversity in more species than the reverse. Furthermore, the variance in chemical composition across samples was higher for fruits than leaves. By documenting a broad pattern of high phytochemical diversity in fruits relative to leaves, this study lays groundwork for incorporating fruit into a comprehensive and integrative understanding of the ecological and evolutionary factors shaping secondary metabolite composition at the whole-plant level.