Many animals produce sounds to communicate with others, often for the purpose of attracting mates, or warning others of nearby predators. Sometimes calls evolve for new and unique purposes, such as the egg-feeding calls of Ranitomeya imitator, otherwise known as the mimic poison frog. While the behaviors surrounding this call have been documented in observations by both natural historians and hobbyist pet owners, the call itself had not been previously characterized. In a new study published in PNAS, Eva Fischer (GNDP), an assistant professor of evolution, ecology, and behavior at the University of Illinois Urbana-Champaign, first-author Jen Moss, a postdoctoral researcher in Fischer’s lab, and James Tumulty, a visiting assistant professor at the College of William & Mary, characterized the acoustic properties of the egg-feeding call in comparison to other well-known male call types for advertisement and courtship. Males and females of this species watch over their eggs until they hatch, after which males carry the tadpoles on their backs and eventually deposit them into small pools of water. The male will then call to the female, signaling her to feed the tadpole an unfertilized egg, called a trophic egg. “The tadpoles are deposited in very small pools of water, often within the leaves of plants,” said Moss. “That’s a strategy for them to avoid competition with other tadpoles and avoid predation. But because these pools are teeny tiny, they don't contain a lot of food, which is why egg-feeding by the female is really important.” The researchers wanted to quantitatively measure acoustic properties of the egg-feeding calls to determine whether they are actually distinct from male calls used for advertising or for courting females. To study this, the team housed frogs in pairs in tanks within the lab and set up microphones and cameras to capture the calls and behaviors of the parents. They recorded advertisement and courtship calls, as well as calls that the males produced in conjunction with egg-feeding by the females, which were labeled as egg-feeding calls. The researchers then characterized and compared the acoustic properties of all the calls, including frequency, bandwidth, and rate of pulses within the spectrum of the calls. Through their analyses, the researchers found that egg-feeding calls share some acoustic properties with courtship and advertisement calls. Both egg-feeding calls and courtship calls were lower in frequency and volume, which the researchers think is because both are used in short-ranged communication. But egg-feeding calls also surprisingly shared some properties with advertisement calls, with the main being that both calls are of longer length compared to courtship calls. The researchers say that a longer call could be important for enhancing the call’s motivational effects — in other words, the longer call could be better at stimulating the female to egg-feed. These findings suggest that egg-feeding calls evolved by co-opting elements of the ancestral call types for advertisement and courtship.
“Capitalizing on various elements from calls already used in the context of a mate attraction and mate communication makes sense, since females are already well attuned to respond,” explained Moss. “But there is also a selective pressure to modify these co-opted signals to be somewhat unique. When females hear these calls, they need to have enough information to know that they’re meant to feed eggs to the tadpoles, as opposed to the courtship context where females are meant to lay eggs that instead develop into tadpoles.” “When we think about how language works, sometimes we come up with an entirely new word, but sometimes we come up with a word by recombining other words and using sounds we already have that together give new meaning,” said Fischer. “For example, the words ‘short’ and ‘stop’ mean very separate things alone, but if you combine them to make the word ‘shortstop’, it’s a separate and new thing. So, you can make something new by recombining things you already have, which is what the frogs have done.” The team also ran a principal component analysis on the calls, which grouped the calls into clusters based on multiple acoustic properties at once. They found that while courtship and advertisement calls had high classification success, the egg-feeding calls clustered more loosely, and overlapped with both of the other call types. According to the researchers, this overlap in acoustic properties with other call types begs the question of how females are consistently able to tell the egg-feeding calls apart. They believe that multimodal signaling, or the use of signals across multiple senses (i.e. hearing, sight, touch), is likely involved. “There's a lot of overlap between the call types and their properties, so clearly, it's not the only thing that females are using to direct their behavior,” said Moss. “We know that that males physically lead females to pools and that tadpoles beg to be fed, so there's probably visual signals involved as well. We don't know very much about whether chemosensory or olfactory communication is involved during this behavior either. So, vocal communication is clearly an important part of the story, but it’s not the full story.” Moss, along with undergraduate Molly Podraza, are currently exploring use of multimodal signaling by the frogs in coordinating parental care. Fischer explained that studying communication by the signaler, in this case the male frog, is important, but that it’s also important to study how the receiver, the female in this case, perceives the signal, which can change depending on context. “Human hearing changes when you become a parent, on the level of the sensory hair cells inside your ear, such that you can identify your baby’s cry over another,” explained Fischer. “And so, another side of this that we haven't explored is whether the females’ perception of the calls also change when they have tadpoles or eggs.” The team says one of the biggest unanswered questions is why the males need to signal for the females to feed the tadpoles in the first place. When the male is removed from the tank, the female will often still carry and feed the tadpoles in his absence. But this doesn’t always happen, and previous work by Tumulty showed that male signaling does have a significant effect on tadpole growth rate. The researchers are currently examining which factors influence if and when females take over parental care in the absence of their male in hopes of answering this question. This research was funded by the NSF and University of Illinois, and can be found at https://doi.org/10.1073/pnas.2218956120.
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CLICK TO READ ON IGB'S WEBSITE Human bodies contain trillions of microbes, so much that the number of microbes rival the number of human cells in a body. These microbes help shape many of our biological functions. For example, microbes in the gut break down food into small molecules called metabolites, many of which are important for human health. Measuring species composition of the microbial community using metagenomics has become a quick and automated process, while measuring the concentrations of metabolites produced by those microbes, a process called metabolomics, is much more difficult and expensive. However, in a new study recently published in Nature Machine Intelligence, researchers have developed a machine learning algorithm called mNODE, which can predict metabolite concentrations based on the species composition of the microbial community. The study was conducted by researchers from IGB’s Center for Artificial Intelligence and Modeling theme, including Yang-Yu Liu (CAIM), an associate professor of Medicine at Harvard Medical School, lead author Tong Wang, a postdoctoral researcher in Liu’s lab, as well Sergei Maslov (CAIM leader/CABBI), a professor of Bioengineering and Physics at the University of Illinois Urbana-Champaign. “Metabolomics requires large and expensive equipment, and it’s not very automated,” said Wang. “It also gives limited information and coverage on the metabolites that actually exist in the community that you’re measuring.” “Next-generation sequencing made genomics about 100,000 times cheaper than it used to be, and led to an increase in genomic sequencing being done,” said Maslov. “But nothing like this happened with measurement of metabolites, so it remains relatively expensive and labor intensive to measure.” Wang started as a graduate student in Maslov’s lab, where he worked on mechanistic models for predicting metabolite concentrations from metagenomic data, with some accuracy. When he became a postdoctoral researcher in Liu’s lab, both labs collaborated to create a machine learning approach to tackle the problem, taking inspiration from another of Liu’s projects which utilized a similar machine learning method. The researchers named the new method mNODE, which stands for Metabolomic profile predictor using Neural Ordinary Differential Equations. “In our earlier mechanistic models, we tried to the best of our ability to model all of the processes of what is produced and who produces what,” Maslov explained. “But as you can imagine, those processes are really complicated, and you need to know hundreds of parameters for each microbial species. But the new machine learning method really comes to the rescue here because it can bypass some of those limitations. And if you have enough data, you can actually predict metabolite concentrations without knowing all of those nitty gritty details.” “Suppose you don’t have the budget to run the expensive metabolomic tests,” explained Liu. “You could instead do very cheap metagenomics sequencing on your microbiome sample, and then use our methods to predict the metabolomic profile. That is the simplest application of our methods. Our long-term goal is to achieve personalized nutrition using our artificial intelligence and machine learning.” First, mNODE was systematically validated using synthetic data generated by models. These models contained ecological data with known interactions between microbes and metabolites. Then, it was applied to real data from various environments. The microbe-metabolite interactions inferred from mNODE were confirmed by comparing them to the results from metabolomics experiments and genomic evidence. “We started with synthetic data because you know exactly where the ground truths are,” said Liu. “Once you’ve finished the validation, you can then apply it to real data. And though you won’t have the complete ground truth, you can compare it to metabolomics tests and genomic information in the literature to validate it.” The researchers say mNODE can not only use microbial composition to predict metabolomic profiles, but it can also incorporate some dietary information to enhance the accuracy of its predictions. They said that although this needs more development, it could be a great tool towards personalized nutrition in healthcare. “If you can integrate this dietary information pretty well, and you know what an ideal metabolic panel looks like, you can then optimize for each individual,” Wang said. “So maybe in the future doctors can give you a dietary recommendation based on your current profile and tell you what is the most ideal combination of different foods to reach a healthy metabolic profile.” “Some metabolites are known as ‘healthy’ metabolites,” Maslov added. “So ideally, you’d want to figure out exactly how to increase the concentration of those in the gut and ultimately the bloodstream of individuals as well. That's the billion-dollar question.” The team described the AI’s success as a testament to the modeling power and innovation of the CAIM, the new theme at the IGB. Maslov explained that projects in the theme aim to have three elements: data generation through experiments, mechanistic models to understand the physical processes behind that data, and machine learning and AI to make predictions based on new data. “This is one of those early success story projects that exemplifies the type of modern science projects that the Center for Artificial Intelligence and Modeling was created for,” Maslov declared. “We believe all three elements are necessary for really successful, modern, and interpretable projects.” Liu added that in the future they hope to design AI for other purposes too, and ones that are not just purely data-driven, but also contain some domain knowledge within biology or physics, which would strengthen the methods. The published study was supported by the National Institutes of Health and can be found at here: https://doi.org/10.1038/s42256-023-00627-3 |