An allometry perspective on crops

Understanding trait-trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges.

Variable Craniofacial Shape and Development among Multiple Cave-Adapted Populations of Astyanax mexicanus

Astyanax mexicanus is a freshwater fish species with blind cave morphs and sighted surface morphs. Like other troglodytic species, independently evolved cave-dwelling A. mexicanus populations share several stereotypic phenotypes, including the expansion of certain sensory systems, as well as the loss of eyes and pigmentation. Here, we assess the extent to which there is also parallelism in craniofacial development across cave populations. Since multiple forces may be acting upon variation in the A. mexicanus system, including phylogenetic history, selection, and developmental constraint, several outcomes are possible. For example, eye regression may have triggered a conserved series of compensatory developmental events, in which case we would expect to observe highly similar craniofacial phenotypes across cave populations. Selection for cave-specific foraging may also lead to the evolution of a conserved craniofacial phenotype, especially in regions of the head directly associated with feeding. Alternatively, in the absence of a common axis of selection or strong developmental constraints, craniofacial shape may evolve under neutral processes such as gene flow, drift, and bottlenecking, in which case patterns of variation should reflect the evolutionary history of A. mexicanus. Our results found that cave-adapted populations do share certain anatomical features; however, they generally did not support the hypothesis of a conserved craniofacial phenotype across caves, as nearly every pairwise comparison was statistically significant, with greater effect sizes noted between more distantly related cave populations with little gene flow. A similar pattern was observed for developmental trajectories. We also found that morphological disparity was lower among all three cave populations versus surface fish, suggesting eye loss is not associated with increased variation, which would be consistent with a release of developmental constraint. Instead, this pattern reflects the relatively low genetic diversity within cave populations. Finally, magnitudes of craniofacial integration were found to be similar among all groups, meaning that coordinated development among anatomical units is robust to eye loss in A. mexicanus. We conclude that, in contrast to many conserved phenotypes across cave populations, global craniofacial shape is more variable, and patterns of shape variation are more in line with population structure than developmental architecture or selection.

Metabolically-driven flows enable exponential growth in macroscopic multicellular yeast

The ecological and evolutionary success of multicellular lineages is due in no small part to their increased size relative to unicellular ancestors. However, large size also poses biophysical challenges, especially regarding the transport of nutrients to all cells; these constraints are typically overcome through multicellular innovations (e.g., a circulatory system). Here we show that an emergent biophysical mechanism - spontaneous fluid flows arising from metabolically-generated density gradients - can alleviate constraints on nutrient transport, enabling exponential growth in nascent multicellular clusters of yeast lacking any multicellular adaptations for nutrient transport or fluid flow. Surprisingly, beyond a threshold size, the metabolic activity of experimentally-evolved snowflake yeast clusters drives large-scale fluid flows that transport nutrients throughout the cluster at speeds comparable to those generated by the cilia of extant multicellular organisms. These flows support exponential growth at macroscopic sizes that theory predicts should be diffusion limited. This work demonstrates how simple physical mechanisms can act as a 'biophysical scaffold' to support the evolution of multicellularity by opening up phenotypic possibilities prior to genetically-encoded innovations. More broadly, our findings highlight how co-option of conserved physical processes is a crucial but underappreciated facet of evolutionary innovation across scales.

Rebuilding the Habitable Zone from the Bottom up with Computational Zones

Computation, if treated as a set of physical processes that act on information represented by states of matter, encompasses biological systems, digital systems, and other constructs and may be a fundamental measure of living systems. The opportunity for biological computation, represented in the propagation and selection-driven evolution of information-carrying organic molecular structures, has been partially characterized in terms of planetary habitable zones (HZs) based on primary conditions such as temperature and the presence of liquid water. A generalization of this concept to computational zones (CZs) is proposed, with constraints set by three principal characteristics: capacity (including computation rates), energy, and instantiation (or substrate, including spatial extent). CZs naturally combine traditional habitability factors, including those associated with biological function that incorporate the chemical milieu, constraints on nutrients and free energy, as well as element availability. Two example applications are presented by examining the fundamental thermodynamic work efficiency and Landauer limit of photon-driven biological computation on planetary surfaces and of generalized computation in stellar energy capture structures (a.k.a. Dyson structures). It is suggested that CZs that involve nested structures or substellar objects could manifest unique observational signatures as cool far-infrared emitters. While these latter scenarios are entirely hypothetical, they offer a useful, complementary introduction to the potential universality of CZs.

Physical constraints during Snowball Earth drive the evolution of multicellularity

Molecular and fossil evidence suggests that complex eukaryotic multicellularity evolved during the late Neoproterozoic era, coincident with Snowball Earth glaciations, where ice sheets covered most of the globe. During this period, environmental conditions-such as seawater temperature and the availability of photosynthetically active light in the oceans-likely changed dramatically. Such changes would have had significant effects on both resource availability and optimal phenotypes. Here, we construct and apply mechanistic models to explore (i) how environmental changes during Snowball Earth and biophysical constraints generated selective pressures, and (ii) how these pressures may have had differential effects on organisms with different forms of biological organization. By testing a series of alternative-and commonly debated-hypotheses, we demonstrate how multicellularity was likely acquired differently in eukaryotes and prokaryotes owing to selective differences on their size due to the biophysical and metabolic regimes they inhabit: decreasing temperatures and resource availability instigated by the onset of glaciations generated selective pressures towards smaller sizes in organisms in the diffusive regime and towards larger sizes in motile heterotrophs. These results suggest that changing environmental conditions during Snowball Earth glaciations gave multicellular eukaryotes an evolutionary advantage, paving the way for the complex multicellular lineages that followed.

Relation of connectome topology to brain volume across 103 mammalian species

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

A Life Outside

How do the morphologies of organisms affect their physical interactions with the environment and other organisms? My research in marine systems couples field studies of the physical habitats, life history strategies, and ecological interactions of organisms with laboratory analyses of their biomechanics. Here, I review how we pursued answers to three questions about marine organisms: (a) how benthic organisms withstand and utilize the water moving around them, (b) how the interaction between swimming and turbulent ambient water flow affects where small organisms go, and (c) how hairy appendages catch food and odors. I also discuss the importance of different types of mentors, the roadblocks for women in science when I started my career, the challenges and delights of interdisciplinary research, and my quest to understand how I see the world as a dyslexic.

Plant science in the age of simulation intelligence

Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as "simulation intelligence", has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.

Scaling of Protein Function across the Tree of Life

Scaling laws are a powerful way to compare genomes because they put all organisms onto a single curve and reveal nontrivial generalities as genomes change in size. The abundance of functional categories across genomes has previously been found to show power law scaling with respect to the total number of functional categories, suggesting that universal constraints shape genomic category abundance. Here, we look across the tree of life to understand how genome evolution may be related to functional scaling. We revisit previous observations of functional genome scaling with an expanded taxonomy by analyzing 3,726 bacterial, 220 archaeal, and 79 unicellular eukaryotic genomes. We find that for some functional classes, scaling is best described by multiple exponents, revealing previously unobserved shifts in scaling as genome-encoded protein annotations increase or decrease. Furthermore, we find that scaling varies between phyletic groups at both the domain and phyla levels and is less universal than previously thought. This variability in functional scaling is not related to taxonomic phylogeny resolved at the phyla level, suggesting that differences in cell plan or physiology outweigh broad patterns of taxonomic evolution. Since genomes are maintained and replicated by the functional proteins encoded by them, these results point to functional degeneracy between taxonomic groups and unique evolutionary trajectories toward these. We also find that individual phyla frequently span scaling exponents of functional classes, revealing that individual clades can move across scaling exponents. Together, our results reveal unique shifts in functions across the tree of life and highlight that as genomes grow or shrink, proteins of various functions may be added or lost.

False Positives and the Challenge of Testing the Alien Hypothesis

The origin of life and the detection of alien life have historically been treated as separate scientific research problems. However, they are not strictly independent. Here, we discuss the need for a better integration of the sciences of life detection and origins of life. Framing these dual problems within the formalism of Bayesian hypothesis testing, we demonstrate via simple examples how high confidence in life detection claims require either (1) a strong prior hypothesis about the existence of life in a particular alien environment, or conversely, (2) signatures of life that are not susceptible to false positives. As a case study, we discuss the role of priors and hypothesis testing in recent results reporting potential detection of life in the venusian atmosphere and in the icy plumes of Enceladus. While many current leading biosignature candidates are subject to false positives because they are not definitive of life, our analyses demonstrate why it is necessary to shift focus to candidate signatures that are definitive. This indicates a necessity to develop methods that lack substantial false positives, by using observables for life that rely on prior hypotheses with strong theoretical and empirical support in identifying defining features of life. Abstract theories developed in pursuit of understanding universal features of life are more likely to be definitive and to apply to life-as-we-don't-know-it. We discuss Molecular Assembly theory as an example of such an observable which is applicable to life detection within the solar system. In the absence of alien examples these are best validated in origin of life experiments, substantiating the need for better integration between origins of life and biosignature science research communities. This leads to a conclusion that extraordinary claims in astrobiology (e.g., definitive detection of alien life) require extraordinary explanations, whereas the evidence itself could be quite ordinary.

Scaling behavior for electric vehicle chargers and road map to addressing the infrastructure gap

Enabling widespread electric vehicle (EV) adoption requires a substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the USA at a county-level resolution. We find that gasoline and EV charging stations scale sub-linearly with their respective vehicle registrations, recovering the sub-linear scaling typical of infrastructure. Surprisingly, we find that EV charging stations scale super-linearly with population size within counties, deviating from the sub-linear scaling of gasoline stations. We discuss how this demonstrates the infancy of both EVs and EV infrastructure while providing a framework for estimating future EV infrastructure demands. By considering the power delivery of existing gasoline stations, and appropriate EV efficiencies, we estimate the EV infrastructure gap at the county level, providing a road map for future EV infrastructure expansion.

Evolution: Mix-and-match adaptations in plant-eating dinosaurs

Ornithischian dinosaurs were primary consumers in Mesozoic ecosystems, their evolution intricately linked to challenges of a plant-heavy diet. Whether phenotypic similarities among different ornithischian lineages imply a common functional solution to herbivory is unclear. New research suggests that they evolved herbivory via multiple biomechanical pathways.

Why isn’t each cell its own cell type? Diminishing returns of increasing cell type diversity can explain cell type allometry

Since the discovery of cells by Robert Hooke and Antoni van Leeuwenhoek in the 17th century, thousands of different cell types have been identified, most recently by sequencing-based single-cell profiling techniques. Yet, for many organisms we still do not know, how many different cell types they are precisely composed of. A recent survey of experimental data, using mostly morphology as a proxy for cell type, revealed allometric scaling of cell type diversity with organism size. Here, I argue from an evolutionary fitness perspective and suggest that three simple assumptions can explain the observed scaling: Evolving a new cell type has, 1. a fitness cost that increases with organism size, 2. a fitness benefit that also increases with organism size but 3. diminishes exponentially with the number of existing cell types. I will show that these assumptions result in a quantitative model that fits the observed cell type numbers across organisms of all size and explains why we should not expect isometric scaling.

Traditional Protocols and Optimization Methods Lead to Absent Expression in a Mycoplasma Cell-Free Gene Expression Platform

Cell-free expression (CFE) systems are one of the main platforms for building synthetic cells. A major drawback is the orthogonality of cell-free systems across species. To generate a CFE system compatible with recently established minimal cell constructs, we attempted to optimize a Mycoplasma bacterium-based CFE system using lysates of the genome-minimized cell JCVI-syn3A (Syn3A) and its close phylogenetic relative Mycoplasma capricolum (Mcap). To produce mycoplasma-derived crude lysates, we systematically tested methods commonly used for bacteria, based on the S30 protocol of Escherichia coli. Unexpectedly, after numerous attempts to optimize lysate production methods or composition of feeding buffer, none of the Mcap or Syn3A lysates supported cell-free gene expression. Only modest levels of in vitro transcription of RNA aptamers were observed. While our experimental systems were intended to perform transcription and translation, our assays focused on RNA. Further investigations identified persistently high ribonuclease (RNase) activity in all lysates, despite removal of recognizable nucleases from the respective genomes and attempts to inhibit nuclease activities in assorted CFE preparations. An alternative method using digitonin to permeabilize the mycoplasma cell membrane produced a lysate with diminished RNase activity yet still was unable to support cell-free gene expression. We found that intact mycoplasma cells poisoned E. coli cell-free extracts by degrading ribosomal RNAs, indicating that the mycoplasma cells, even the minimal cell, have a surface-associated RNase activity. However, it is not clear which gene encodes the RNase. This work summarizes attempts to produce mycoplasma-based CFE and serves as a cautionary tale for researchers entering this field.

Graphical Abstract

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Scaling laws in enzyme function reveal a new kind of biochemical universality

Known examples of life all share the same core biochemistry going back to the last universal common ancestor (LUCA), but whether this feature is universal to other examples, including at the origin of life or alien life, is unknown. We show how a physics-inspired statistical approach identifies universal scaling laws across biochemical reactions that are not defined by common chemical components but instead, as macroscale patterns in the reaction functions used by life. The identified scaling relations can be used to predict statistical features of LUCA, and network analyses reveal some of the functional principles that underlie them. They are, therefore, prime candidates for developing new theory on the “laws of life” that might apply to all possible biochemistries.

All life on Earth is unified by its use of a shared set of component chemical compounds and reactions, providing a detailed model for universal biochemistry. However, this notion of universality is specific to known biochemistry and does not allow quantitative predictions about examples not yet observed. Here, we introduce a more generalizable concept of biochemical universality that is more akin to the kind of universality found in physics. Using annotated genomic datasets including an ensemble of 11,955 metagenomes, 1,282 archaea, 11,759 bacteria, and 200 eukaryotic taxa, we show how enzyme functions form universality classes with common scaling behavior in their relative abundances across the datasets. We verify that these scaling laws are not explained by the presence of compounds, reactions, and enzyme functions shared across known examples of life. We demonstrate how these scaling laws can be used as a tool for inferring properties of ancient life by comparing their predictions with a consensus model for the last universal common ancestor (LUCA). We also illustrate how network analyses shed light on the functional principles underlying the observed scaling behaviors. Together, our results establish the existence of a new kind of biochemical universality, independent of the details of life on Earth’s component chemistry, with implications for guiding our search for missing biochemical diversity on Earth or for biochemistries that might deviate from the exact chemical makeup of life as we know it, such as at the origins of life, in alien environments, or in the design of synthetic life.

Ecological biomechanics of marine macrophytes

Macroalgae and seagrasses in coastal habitats are exposed to turbulent water currents and waves that deform them and can rip them off the substratum, but that also transport essential water-borne substances to them and disperse their propagules and wastes. Field studies of the physical environment, ecological interactions, and life history strategies of marine macrophytes reveal which aspects of their biomechanical performance are important to their success in different types of natural habitats and enable us to design ecologically relevant laboratory experiments to study biomechanical function. Morphology and tissue mechanical properties determine the hydrodynamic forces on macrophytes and their fate when exposed to those forces, but different mechanical designs can perform well in the same biophysical habitat. There is a trade-off between maximizing photosynthesis and minimizing breakage, and some macrophytes change their morphology in response to environmental cues. Water flow in marine habitats varies on a wide range of temporal and spatial scales, so diverse flow microhabitats can occur at the same site. Likewise, the size, shape, and tissue material properties of macrophytes change as they grow and age, so it is important to understand the different physical challenges met by macrophytes throughout their lives.

Emergency of Tsallis statistics in fractal networks

Scale-free networks constitute a fast-developing field that has already provided us with important tools to understand natural and social phenomena. From biological systems to environmental modifications, from quantum fields to high energy collisions, or from the number of contacts one person has, on average, to the flux of vehicles in the streets of urban centres, all these complex, non-linear problems are better understood under the light of the scale-free network’s properties. A few mechanisms have been found to explain the emergence of scale invariance in complex networks, and here we discuss a mechanism based on the way information is locally spread among agents in a scale-free network. We show that the correct description of the information dynamics is given in terms of the q-exponential function, with the power-law behaviour arising in the asymptotic limit. This result shows that the best statistical approach to the information dynamics is given by Tsallis Statistics. We discuss the main properties of the information spreading process in the network and analyse the role and behaviour of some of the parameters as the number of agents increases. The different mechanisms for optimization of the information spread are discussed.

The Multiple Paths to Multiple Life

We argue for multiple forms of life realized through multiple different historical pathways. From this perspective, there have been multiple origins of life on Earth—life is not a universal homology. By broadening the class of originations, we significantly expand the data set for searching for life. Through a computational analogy, the origin of life describes both the origin of hardware (physical substrate) and software (evolved function). Like all information-processing systems, adaptive systems possess a nested hierarchy of levels, a level of function optimization (e.g., fitness maximization), a level of constraints (e.g., energy requirements), and a level of materials (e.g., DNA or RNA genome and cells). The functions essential to life are realized by different substrates with different efficiencies. The functional level allows us to identify multiple origins of life by searching for key principles of optimization in different material form, including the prebiotic origin of proto-cells, the emergence of culture, economic, and legal institutions, and the reproduction of software agents.

Generalized Stoichiometry and Biogeochemistry for Astrobiological Applications

A central need in the field of astrobiology is generalized perspectives on life that make it possible to differentiate abiotic and biotic chemical systems McKay (2008). A key component of many past and future astrobiological measurements is the elemental ratio of various samples. Classic work on Earth's oceans has shown that life displays a striking regularity in the ratio of elements as originally characterized by Redfield (Redfield 1958; Geider and La Roche 2002; Eighty years of Redfield 2014). The body of work since the original observations has connected this ratio with basic ecological dynamics and cell physiology, while also documenting the range of elemental ratios found in a variety of environments. Several key questions remain in considering how to best apply this knowledge to astrobiological contexts: How can the observed variation of the elemental ratios be more formally systematized using basic biological physiology and ecological or environmental dynamics? How can these elemental ratios be generalized beyond the life that we have observed on our own planet? Here, we expand recently developed generalized physiological models (Kempes et al. 2012, 2016, 2017, 2019) to create a simple framework for predicting the variation of elemental ratios found in various environments. We then discuss further generalizing the physiology for astrobiological applications. Much of our theoretical treatment is designed for in situ measurements applicable to future planetary missions. We imagine scenarios where three measurements can be made-particle/cell sizes, particle/cell stoichiometry, and fluid or environmental stoichiometry-and develop our theory in connection with these often deployed measurements.

Growth, death, and resource competition in sessile organisms

Population-level scaling in ecological systems arises from individual growth and death with competitive constraints. We build on a minimal dynamical model of metabolic growth where the tension between individual growth and mortality determines population size distribution. We then separately include resource competition based on shared capture area. By varying rates of growth, death, and competitive attrition, we connect regular and random spatial patterns across sessile organisms from forests to ants, termites, and fairy circles. Then, we consider transient temporal dynamics in the context of asymmetric competition, such as canopy shading or large colony dominance, whose effects primarily weaken the smaller of two competitors. When such competition couples slow timescales of growth to fast competitive death, it generates population shocks and demographic oscillations similar to those observed in forest data. Our minimal quantitative theory unifies spatiotemporal patterns across sessile organisms through local competition mediated by the laws of metabolic growth, which in turn, are the result of long-term evolutionary dynamics.

Toward Engineering Biosystems With Emergent Collective Functions

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

Circumventing surface tension: tadpoles suck bubbles to breathe air

The surface tension of water provides a thin, elastic membrane upon which many tiny animals are adapted to live and move. We show that it may be equally important to the minute animals living beneath it by examining air-breathing mechanics in five species (three families) of anuran (frog) tadpoles. Air-breathing is essential for survival and development in most tadpoles, yet we found that all tadpoles at small body sizes were unable to break through the water's surface to access air. Nevertheless, by 3 days post-hatch and only 3 mm body length, all began to breathe air and fill the lungs. High-speed macrovideography revealed that surface tension was circumvented by a novel behaviour we call 'bubble-sucking': mouth attachment to the water's undersurface, the surface drawn into the mouth by suction, a bubble 'pinched off' within the mouth, then compressed and forced into the lungs. Growing tadpoles transitioned to air-breathing via typical surface breaching. Salamander larvae and pulmonate snails were also discovered to 'bubble-suck', and two insects used other means of circumvention, suggesting that surface tension may have a broader impact on animal phenotypes than hitherto appreciated.