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Young precocial birds benefit from staying close to both their mother and siblings, while prioritising adults, which provide better care. Which features of the stimuli are used by young birds to prioritise attachment to adults over siblings is unknown. We started to address this question in newly hatched domestic chicks (Gallus gallus), focusing on their spontaneous preferences for visual stimuli that systematically vary between adult and juvenile chickens: size (larger in adults than in juveniles) and colour (darker and redder in adults than in juveniles). Overall, chicks at their first visual experience, that had never seen a conspecific beforehand, were most attracted to the red and large stimuli (two adult features) and interacted with red stimuli more than with yellow stimuli. When tested with red large vs. small objects (Exp. 1), chicks preferred the large shape. When tested with yellow large and small objects (Exp. 2), chicks did not show a preference. These results suggest that the combination of size and colour form the predisposition that helps chicks to spontaneously discriminate between adult and juvenile features from the first stages of life, in the absence of previous experience.

At the beginning of life, inexperienced animals use evolutionary-given preferences (predispositions) to decide what stimuli to attend and approach. Stimuli that contain cues of animacy, such as face-like stimuli, biological motion and changes in speed, are particularly attractive across vertebrate taxa. A strong cue of animacy is upward movement against terrestrial gravity, because only animate objects consistently move upward. To test whether upward movement is spontaneously considered attractive already at birth, we tested the early preferences of dark-hatched chicks ( Gallus gallus ) for upward- versus downward-moving visual stimuli. We found that, without any previous visual experience, chicks consistently exhibited a preference to approach stimuli that move upward, against gravity. A control experiment showed that these preferences are not driven by avoidance of downward stimuli. These results show that newborn animals have a gravity prior that attracts them toward upward movement. Movement against gravity can be used as a cue of animacy to orient early approach responses in the absence of previous visual experience.

Plants, animals, and fungi display a rich tapestry of colors. Animals, in particular, use colors in dynamic displays performed in spatially complex environments. In such natural settings, light is reflected or refracted from objects with complex shapes that cast shadows and generate highlights. In addition, the illuminating light changes continuously as viewers and targets move through heterogeneous, continually fluctuating, light conditions. Although traditional spectrophotometric approaches for studying colors are objective and repeatable, they fail to document this complexity. Worse, they miss the temporal variation of color signals entirely. Here, we introduce hardware and software that provide ecologists and filmmakers the ability to accurately record animal-perceived colors in motion. Specifically, our Python codes transform photos or videos into perceivable units (quantum catches) for any animal of known photoreceptor sensitivity. We provide the plans, codes, and validation tests necessary for end-users to capture animal-view videos. This approach will allow ecologists to investigate how animals use colors in dynamic behavioral displays, the ways natural illumination alters perceived colors, and other questions that remained unaddressed until now due to a lack of suitable tools. Finally, our pipeline provides scientists and filmmakers with a new, empirically grounded approach for depicting the perceptual worlds of non-human animals.

At the beginning of life, inexperienced animals use evolutionary-given preferences (predispositions) to decide what stimuli attend and approach. Stimuli that contain cues of animacy, such as face-like stimuli, biological motion and changes in speed, are particularly attractive across vertebrate taxa. A strong cue of animacy is upward movement against terrestrial gravity, because only animate objects consistently move upward. To test whether upward movement is spontaneously considered attractive already at birth, we tested the early preferences of dark-hatched chicks (Gallus gallus) for upward vs downward moving visual stimuli. We found that, without any previous visual experience, chicks consistently exhibited a preference to approach upward moving stimuli, that move against gravity. A control experiment showed that these preferences are not driven by avoidance of downward stimuli. These results show that newborn animals are spontaneously attracted by upward movement, indicating that movement against gravity can be used as a cue of animacy to orient early approach responses in the absence of previous visual experience.

True colour vision requires comparing the responses of different spectral classes of photoreceptors. In insects, there is a wealth of data available on the physiology of photoreceptors and on colour-dependent behaviour, but less is known about the neural mechanisms that link the two. The available information in bees indicates a diversity of colour opponent neurons in the visual optic ganglia that significantly exceeds that known in humans and other primates. Here, we present a simple mathematical model for colour processing in the optic lobes of bees to explore how this diversity might arise. We found that the model can reproduce the physiological spectral tuning curves of the 22 neurons that have been described so far. Moreover, the distribution of the presynaptic weights in the model suggests that colour-coding neurons are likely to be wired up to the receptor inputs randomly. The perceptual distances in our random synaptic weight model are in agreement with behavioural observations. Our results support the idea that the insect nervous system might adopt partially random wiring of neurons for colour processing.

Foraging animals, such as bees, can have access to both personal and social information. Knowing when it is advantageous to copy others, and when it is better to draw your own conclusions, is crucial for decision making. We show that when bumblebees face an easy foraging task, their decisions are only marginally affected by the behavior of others. On the contrary, bees rely on social information when solving a difficult foraging task.

Predator mimicry occurs when prey resemble their predator to gain protection. We explored the relative importance of the morphological and locomotor signals that spider-mimicking moths use to deceive their jumping spider predators. Two hypotheses explain why animals use multicomponent signals for communication: the ‘back-up signal’ hypothesis which suggests that multiple traits increase accuracy, and the ‘multiple message’ hypothesis which suggests that the different traits serve different purposes or target different signal receivers. We conducted predation tests using the putative spider-mimicking moths Brenthia coronigera (visual and locomotor mimicry) and Choreutis hyligenes (only locomotor mimicry) and a control moth species displaying no mimicry. We found that B. coronigera used multicomponent signals, i.e. pattern, display posture and jumping behaviour, to deceive its jumping spider predators, and thus experienced lower predation rates and more time for escaping. Spiders suffered a decreased predation rate when they encountered B. coronigera, relative to the other two moth species. Spiders displayed leg-waving behaviour (which is used in courtship and territorial display) to both live and lure B. coronigera, suggesting that the spiders considered the moths to be another jumping spider. When the eyespots of B. coronigera were erased, the predation rate increased. In addition, the latency of first attacks was significantly longer in live B. coronigera moths than in lures fixed in the display posture. This suggests that the eyespots, the ‘peacock-like’ display position and the jumping movement all add to the similarity with jumping spiders. Our results support the ‘back-up signal’ hypothesis: that multiple signals can deceive the predators better. Our experimental paradigm enabled us to explore the recognition ability of predators, and gave insight into the ways evolution shapes the mimicry system.

A basic property of life is its capacity to experience Darwinian evolution. The replicator concept is at the core of genetics-first theories of the origin of life, which suggest that self-replicating oligonucleotides or their similar ancestors may have been the first “living” systems and may have led to the evolution of an RNA world. But problems with the nonenzymatic synthesis of biopolymers and the origin of template replication have spurred the alternative metabolism-first scenario, where self-reproducing and evolving proto-metabolic networks are assumed to have predated self-replicating genes. Recent theoretical work shows that “compositional genomes” (i.e., the counts of different molecular species in an assembly) are able to propagate compositional information and can provide a setup on which natural selection acts. Accordingly, if we stick to the notion of replicator as an entity that passes on its structure largely intact in successive replications, those macromolecular aggregates could be dubbed “ensemble replicators” (composomes) and quite different from the more familiar genes and memes. In sharp contrast with template-dependent replication dynamics, we demonstrate here that replication of compositional information is so inaccurate that fitter compositional genomes cannot be maintained by selection and, therefore, the system lacks evolvability (i.e., it cannot substantially depart from the asymptotic steady-state solution already built-in in the dynamical equations). We conclude that this fundamental limitation of ensemble replicators cautions against metabolism-first theories of the origin of life, although ancient metabolic systems could have provided a stable habitat within which polymer replicators later evolved.

Species that have outstanding importance in the functioning of a community are called keystone species. Network indices are increasingly used to identify them, e.g. for conservation biological purposes. The problem is that the calculation of these indices is based on the particular network model of the studied food web, which can include network construction errors. For example, additional, unnecessary trophic links can be built in, or, to the contrary, functional links can be left out. What is the effect of such errors on the result of network analysis, e.g. the centrality values of species? Can you rely on the importance rank of species that you calculated? We developed a robustness measure (R) for network indices to answer these questions. R is proportional to the likeliness that the importance rank of nodes in the given network according to a given index would not change due to possible errors in network construction. For calculating R, first the maximum expected error (P) has to be computed which represents the potential range of error in estimating the keystone index in question. Basically, R is calculated by comparing P to the keystone indices of species to assess the reliability of the importance rank of species based on the network model. We calculated the robustness of 13 different structural indices in 26 food webs of different size to test the P and R values. We found that fragmentation indices and the number of dominated nodes can be characterized by quite low R values, while betweenness, topological importance, keystoneness and mixed trophic impact have high R values, which means that they are relatively more reliable for assessing the importance rank of species in an uncertain network model. However, as R was found to be very variable, depending on the topology of a given network, a detailed description is provided for performing the actual calculations case-by-case.

Maintaining connectivity among local populations in a fragmented landscape is crucial for the survival of many species. For isolated habitat patches, stochastic fluctuations and reduced gene flow can lead to high risk of extinction. The connectivity of the landscape is especially crucial for the carabid species living in the fragmented forests of the Bereg plain (NE Hungary and W Ukraine) because a highway will be constructed through the plain. Our purpose is to (1) evaluate the impacts of three possible highway tracks, (2) suggest a solution that is realistic with less impact on connectivity than other plans and (3) discuss how to decrease the disadvantageous effects of each track. Our results, based on a network analysis of landscape graph of patches and ecological corridors, indicate that the intended highway could have deleterious consequences on forest-living carabids. Relatively simple actions, like the establishment of stepping stones, could compensate for the loss of habitat connectivity and promote the survival of carabids, or minor modifications in one possible track could diminish its adverse effects. While many other studies would be needed for a comprehensive assessment of the biotic impact of the highway, we provide an example on the usefulness of network analysis for land use management.

For many species, one important key to persistence is maintaining connectivity among local populations that allow for dispersal and gene flow. This is probably true for carabid species (Coleoptera:Carabidae) living in the fragmented forests of the Bereg Plain (NE Hungary and W Ukraine). Based on field data, we have drafted a landscape graph of the area representing the habitat network of these species. Graph nodes and links represented two kinds of landscape elements: habitat (forest) patches and corridors, respectively. The quality of habitat patches and corridors were ranked (from low (1) to high (4)), reflecting local population sizes in the case of patches and estimated permeability in the case of corridors. We analysed (1) the positional importance of landscape elements in maintaining the connectivity of the intact network, (2) the effect of inserting hypothetical corridors into the network, (3) the effects of improving the quality of the existing corridors, and (4) how to connect every patch in a cost-effective way. Our results set quantitative priorities for conservation practice by identifying important corridors: what to protect, what to build and what to improve. Several network analytical techniques were used to account for the directed (source-sink) and highly fragmented nature of the landscape graph. We provide conservation priority ranks for the landscape elements and discuss the conditions for the use of particular network indices. Our study could be of extreme relevance, since a new highway is being planned through the area.

We investigated the effects of human activ- ities on the pelagic food web structure of nearshore marine ecosystems. Their generic structure was estab- lished on the basis of literature review and analyzed by qualitative structural network analysis. Two main issues were addressed: (1) the role of species capable of forming harmful algal blooms (HABs) and red tides (Noctiluca spp.), as well as the role of jellyfish, in eutrophicated sys- tems; (2) the contribution of human influences on food webs, focusing on bottom-up (increased nutrient loading) and top-down (overfishing) effects. Results suggest that HAB-forming species and Noctiluca stimulate the micro- bial network, but reduce higher trophic levels such as commercially important fish species. Jellyfish act as a buffer in eutrophicated and overfished systems, as they retain nutrients from the water column, but their blooms lead to a massive accumulation of large phytoplankton organisms. Anthropogenic nutrient enrichment favors undesirable species because of their specific position in the food web, and this crucial position may explain their far-reaching effects. Finally, while it appears that over- fishing of piscivorous fishes inhibited HABs and sup- ported blooms of diatoms and other large algae in the past, the present-day loss of planktivorous fishes acts syn- ergistically with nutrient enrichment in promoting HAB species, Noctiluca and jellyfish. These fundamental con- straints, which are inherent in the generic structure of pelagic food webs, thus largely determine community dynamics in marine coastal ecosystems.

While it is generally agreed that some kind of replicating non-living compounds were the precursors of life, there is much debate over their possible chemical nature. Metabolism-first approaches propose that mutually catalytic sets of simple organic molecules could be capable of self-replication and rudimentary chemical evolution. In particular, the graded autocatalysis replication domain (GARD) model, depicting assemblies of amphiphilic molecules, has received considerable interest. The system propagates compositional information across generations and is suggested to be a target of natural selection. However, evolutionary simulations indicate that the system lacks selectability (i.e. selection has negligible effect on the equilibrium concentrations). We elaborate on the lessons learnt from the example of the GARD model and, more widely, on the issue of evolvability, and discuss the implications for similar metabolism-first scenarios. We found that simple incorporation-type chemistry based on non-covalent bonds, as assumed in GARD, is unlikely to result in alternative autocatalytic cycles when catalytic interactions are randomly distributed. An even more serious problem stems from the lognormal distribution of catalytic factors, causing inherent kinetic instability of such loops, due to the dominance of efficiently catalyzed components that fail to return catalytic aid. Accordingly, the dynamics of the GARD model is dominated by strongly catalytic, but not auto-catalytic, molecules. Without effective autocatalysis, stable hereditary propagation is not possible. Many repetitions and different scaling of the model come to no rescue. Despite all attempts to show the contrary, the GARD model is not evolvable, in contrast to reflexively autocatalytic networks, complemented by rare uncatalyzed reactions and compartmentation. The latter networks, resting on the creation and breakage of chemical bonds, can generate novel ('mutant') autocatalytic loops from a given set of environmentally available compounds. Real chemical reactions that make or break covalent bonds, rather than mere incorporation of components, are necessary for open-ended evolvability. The issue of whether or not several concrete chemical systems (rather than singular curiosities) could realize reflexively autocatalytic macromolecular networks will ultimately determine the relevance of metabolism-first approaches to the origin of life, as stepping stones towards true open-endedness that requires the combination of rich combinatorial chemistry controlled by information stored in template replicators.

There is increasing evidence that non-trophic interspecific interactions play an at least as important role in community dynamics as trophic relationships. More and more studies on pollination, mutualism and facilitation are published but these effects are interpreted more like alternative explanations than being synthesized with results of trophic analyses. Here, we construct and analyze the interaction web of the well-studied Chesapeake Bay mesohaline ecosystem. By interaction web we mean a food web completed by a carefully selected set of non-trophic links. We quantify the interaction structure of the web and the positional importance of nodes by different network indices. We perform the suitable analyses for different variants of the network: combinations of direction, sign and weights, as well as considering also non-trophic links result in a set of webs of different information content. We also create a semi-quantitative variant of the web, in which only the order of magnitude of the mass flows are considered. The appropriate network indices for each web variant are calculated and compared. Finally, however our paper is primarily of methodological nature, we present some findings about the fish community of the Bay. We suggest that the multiple techniques presented here, adapted even from social network analysis, can help field conservation efforts by suggesting optimal preferences for data collection.

Link distribution is an important architectural feature of ecological networks, since it is thought to influence community dynamics. Several attempts have been made in order to characterize the typical link distribution of food webs, but the number of webs studied thus far is low and their quality is unbalanced. Comparability is a rarely asked methodological question, and as far as we see only two data bases are available which allow reliable comparison of food webs: one for terrestrial, high resolution, host-parasitoid webs and another for highly aggregated, marine trophic networks. We present an analysis of a set of food webs belonging to the latter type, since the host-parasitoid networks are only subgraphs and therefore uninformative on the structure of the entire community. We address the following three questions: (1) how to characterize the link distribution of these small networks which cannot always be fitted statistically to well-known distributions (such as the exponential or the Poisson, etc.), (2) are these distributions of more or less similar shape or they belong to different „architectural classes”, and (3) if there are different classes, then what are their distinctive topological and biological properties. We suggest that link distribution of such small networks can be compared to each other by principal coordinates ordination and clustering. We conclude that (1) the webs can be categorized into two different classes, and (2) one of the classes contains significantly larger and topologically more heterogeneous webs for which net output of material is also of higher variance. We emphasize that link distribution is an interesting and important property not only in case of complex, speciose food webs, but also in highly aggregated, low-resolution webs.

A mechanism of Darwinian chemical evolution is proposed based on cooptation of chemical avalanches occurring in a catalytic network of molecules enclosed in protocell compartments. This intermediate stage of information transmission would have been of importance prior to template replication and may be necessary to explain the origin of highly complex molecules such as nucleotides capable of template replication.

All life, as we know it, springs from already existing life. But how did it all begin? We argue that evolution preceded life, and we must understand the origin of evolvable chemical systems as their gradual increase of complexity lead to the appearance of the first living entity. In this chapter, we discuss various theoretical models that have been proposed to explain the origin of evolvable systems (including template replicators, autocatalytic cycles, polymer autocatalytic sets, and the GARD model) and translate the critical properties of evolution to features of chemical networks.

Background Our current understanding of evolution is so tightly linked to template-dependent replication of DNA and RNA molecules that the old idea from Oparin of a self-reproducing 'garbage bag' ('coacervate') of chemicals that predated fully-fledged cell-like entities seems to be farfetched to most scientists today. However, this is exactly the kind of scheme we propose for how Darwinian evolution could have occurred prior to template replication. Results We cannot confirm previous claims that autocatalytic sets of organic polymer molecules could undergo evolution in any interesting sense by themselves. While we and others have previously imagined inhibition would result in selectability, we found that it produced multiple attractors in an autocatalytic set that cannot be selected for. Instead, we discovered that if general conditions are satisfied, the accumulation of adaptations in chemical reaction networks can occur. These conditions are the existence of rare reactions producing viable cores (analogous to a genotype), that sustains a molecular periphery (analogous to a phenotype). Conclusions We conclude that only when a chemical reaction network consists of many such viable cores, can it be evolvable. When many cores are enclosed in a compartment there is competition between cores within the same compartment, and when there are many compartments, there is between-compartment competition due to the phenotypic effects of cores and their periphery at the compartment level. Acquisition of cores by rare chemical events, and loss of cores at division, allows macromutation, limited heredity and selectability, thus explaining how a poor man's natural selection could have operated prior to genetic templates. This is the only demonstration to date of a mechanism by which pre-template accumulation of adaptation could occur. Reviewers This article was reviewed by William Martin and Eugene Koonin.

We present a novel algorithm that exhibits natural selection of paths in a network. If each node and weighted directed edge has a unique identifier, a path in the network is defined as an ordered list of these unique identifiers. We take a population perspective and view each path as a genotype. If each node has a node phenotype then a path phenotype is defined as the list of node phenotypes in order of traversal. We show that given appropriate path traversal, weight change and structural plasticity rules, a path is a unit of evolution because it can exhibit multiplicative growth (i.e. change it’s probability of being traversed), and have variation and heredity. Thus, a unit of evolution need not be a spatially distinct physical individual. The total set of paths in a network consists of all possible paths from the start node to a finish node. Each path phenotype is associated with a reward that determines whether the edges of that path will be multiplicatively strengthened (or weakened). A pair-wise tournament selection algorithm is implemented which compares the reward obtained by two paths. The directed edges of the winning path are strengthened, whilst the directed edges of the losing path are weakened. Edges shared by both paths are not changed (or weakened if diversity is desired). Each time a node is activated there is a probability that the path will mutate, i.e. find an alternative route that bypasses that node. This generates the potential for a novel but correlated path with a novel but correlated phenotype. By this process the more frequently traversed paths are responsible for most of the exploration. Nodes that are inactive for some period of time are lost (which is equivalent to connections to and from them being broken). This network-based natural selection compares favourably with a standard pair-wise tournament-selection based genetic algorithm on a range of combinatorial optimization problems and continuous parametric optimization problems. The network also exhibits memory of past selective environments and can store previously discovered characters for reuse in later optimization tasks. The pathway evolution algorithm has several possible implementations and permits natural selection with unlimited heredity without template replication.

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ‘memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells., Author Summary Whilst one may have believed that associative learning requires a nervous system, this paper shows that chemical networks can be evolved in silico to undertake a range of associative learning tasks with only a small number of reactions. The mechanisms are surprisingly simple. The networks can be analysed using Bayesian methods to identify the components of the network responsible for learning. The networks evolved were simpler in some ways to hand-designed synthetic biology networks for associative learning. The motifs may be looked for in biochemical networks and the hypothesis that they undertake associative learning, e.g. in single cells or during development may be legitimately entertained.

Many pollinating insects acquire their entire nutrition from visiting flowers, and they must therefore be efficient both at detecting flowers and at recognizing familiar rewarding flower types. A crucial first step in recognition is the identification of edges and the segmentation of the visual field into areas that belong together. Honeybees and bumblebees acquire visual information through three types of photoreceptors; however, they only use a single receptor type—the one sensitive to longer wavelengths—for edge detection and movement detection. Here, we show that these long-wavelength receptors (peak sensitivity at ~544 nm, i.e., green) provide the most consistent signals in response to natural objects. Using our multispectral image database of flowering plants, we found that long-wavelength receptor responses had, depending on the specific scenario, up to four times higher signal-to-noise ratios than the short- and medium-wavelength receptors. The reliability of the long-wavelength receptors emerges from an intricate interaction between flower coloration and the bee’s visual system. This finding highlights the adaptive significance of bees using only long-wavelength receptors to locate flowers among leaves, before using information provided by all three receptors to distinguish the rewarding flower species through trichromatic color vision. Electronic supplementary material The online version of this article (doi:10.1007/s00359-017-1156-x) contains supplementary material, which is available to authorized users.