Inferring predator-prey interaction in the subterranean environment: a case study from Dinaric caves.

Predator-prey interactions are among the most important biotic interactions shaping ecological communities and driving the evolution of defensive traits. These interactions and their effects on species received little attention in extreme and remote environments, where possibilities for direct observations and experimental manipulation of the animals are limited. In this paper, we study such type of environment, namely caves of the Dinarides (Europe), combining spatial and phylogenetic methods. We focused on several species of Niphargus amphipods living in phreatic lakes, as some of them use the dorsal spines as putative morphological defensive traits. We predicted that these spines represent a defense strategy against the olm (Proteus anguinus), a top predator species in the subterranean waters. We tested for spatial overlap of the olm and Niphargus species and showed that spined species live in closer proximity to and co-occur more frequently with the olm than non-spined species. Modeling of the evolution of the spines onto Niphargus phylogeny implies coevolution of this trait in the presence of olm. We conclude that these spines likely evolved as defensive traits in a predator-prey arms race. Combining multiple analyses, we provide an example for a methodological framework to assess predator-prey interactions when in-situ or laboratory observations are not possible.

Cardenolides, toxicity, and the costs of sequestration in the coevolutionary interaction between monarchs and milkweeds.

For highly specialized insect herbivores, plant chemical defenses are often co-opted as cues for oviposition and sequestration. In such interactions, can plants evolve novel defenses, pushing herbivores to trade off benefits of specialization with costs of coping with toxins? We tested how variation in milkweed toxins (cardenolides) impacted monarch butterfly (Danaus plexippus) growth, sequestration, and oviposition when consuming tropical milkweed (Asclepias curassavica), one of two critical host plants worldwide. The most abundant leaf toxin, highly apolar and thiazolidine ring-containing voruscharin, accounted for 40% of leaf cardenolides, negatively predicted caterpillar growth, and was not sequestered. Using whole plants and purified voruscharin, we show that monarch caterpillars convert voruscharin to calotropin and calactin in vivo, imposing a burden on growth. As shown by in vitro experiments, this conversion is facilitated by temperature and alkaline pH. We next employed toxin-target site experiments with isolated cardenolides and the monarch's neural Na+/K+-ATPase, revealing that voruscharin is highly inhibitory compared with several standards and sequestered cardenolides. The monarch's typical >50-fold enhanced resistance to cardenolides compared with sensitive animals was absent for voruscharin, suggesting highly specific plant defense. Finally, oviposition was greatest on intermediate cardenolide plants, supporting the notion of a trade-off between benefits and costs of sequestration for this highly specialized herbivore. There is apparently ample opportunity for continued coevolution between monarchs and milkweeds, although the diffuse nature of the interaction, due to migration and interaction with multiple milkweeds, may limit the ability of monarchs to counteradapt.

Deep learning on butterfly phenotypes tests evolution's oldest mathematical model.

Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology's oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.

Approximate Bayesian estimation of coevolutionary arms races.

Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race. Despite decades of research, however, we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration. Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection. Here we develop an approximate Bayesian computation (ABC) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions. Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations, variable abiotic environments, and strong coevolutionary selection. Using simulated data, we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled. Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil, Curculio camelliae, provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution. Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0.941. The strong agreement between predicted and observed selection gradients validates our method.

Helicobacter pylori in human health and disease: Mechanisms for local gastric and systemic effects.

Helicobacter pylori (H. pylori) is present in roughly 50% of the human population worldwide and infection levels reach over 70% in developing countries. The infection has classically been associated with different gastro-intestinal diseases, but also with extra gastric diseases. Despite such associations, the bacterium frequently persists in the human host without inducing disease, and it has been suggested that H. pylori may also play a beneficial role in health. To understand how H. pylori can produce such diverse effects in the human host, several studies have focused on understanding the local and systemic effects triggered by this bacterium. One of the main mechanisms by which H. pylori is thought to damage the host is by inducing local and systemic inflammation. However, more recently, studies are beginning to focus on the effects of H. pylori and its metabolism on the gastric and intestinal microbiome. The objective of this review is to discuss how H. pylori has co-evolved with humans, how H. pylori presence is associated with positive and negative effects in human health and how inflammation and/or changes in the microbiome are associated with the observed outcomes.

Population size changes and selection drive patterns of parallel evolution in a host-virus system.

Predicting the repeatability of evolution remains elusive. Theory and empirical studies suggest that strong selection and large population sizes increase the probability for parallel evolution at the phenotypic and genotypic levels. However, selection and population sizes are not constant, but rather change continuously and directly affect each other even on short time scales. Here, we examine the degree of parallel evolution shaped through eco-evolutionary dynamics in an algal host population coevolving with a virus. We find high degrees of parallelism at the level of population size changes (ecology) and at the phenotypic level between replicated populations. At the genomic level, we find evidence for parallelism, as the same large genomic region was duplicated in all replicated populations, but also substantial novel sequence divergence between replicates. These patterns of genome evolution can be explained by considering population size changes as an important driver of rapid evolution.

Innate olfactory preferences for flowers matching proboscis length ensure optimal energy gain in a hawkmoth.

Cost efficient foraging is of especial importance for animals like hawkmoths or hummingbirds that are feeding 'on the wing', making their foraging energetically demanding. The economic decisions made by these animals have a strong influence on the plants they pollinate and floral volatiles are often guiding these decisions. Here we show that the hawkmoth Manduca sexta exhibits an innate preference for volatiles of those Nicotiana flowers, which match the length of the moth's proboscis. This preference becomes apparent already at the initial inflight encounter, with the odour plume. Free-flight respiration analyses combined with nectar calorimetry revealed a significant caloric gain per invested flight energy only for preferred-matching-flowers. Our data therefore support Darwin's initial hypothesis on the coevolution of flower length and moth proboscis. We demonstrate that this interaction is mediated by an adaptive and hardwired olfactory preference of the moth for flowers offering the highest net-energy reward.