A sense of scale: Foraging cetaceans' use of scale‐dependent multimodal sensory systems

Research on cetacean foraging ecology is central to our understanding of their spatial and behavioral ecology. Yet, functional mechanisms by which cetaceans detect prey across different scales remain unclear. Here, I postulate that cetaceans utilize a scale‐dependent, multimodal sensory system to assess and increase prey encounters. I review the literature on cetacean sensory systems related to foraging ecology, and hypothesize the effective scales of each sensory modality to inform foraging opportunities. Next, I build two “scale‐of‐senses” schematics for the general groups of dolphins and baleen whales. These schematics illustrate the hypothetical interchange of sensory modalities used to locate and discriminate prey at spatial scales ranging from 0 m to 1,000 km: (1) vision, (2) audition (sound production and sound reception), (3) chemoreception, (4) magnetoreception, and somatosensory perception of (5) prey, or (6) oceanographic stimuli. The schematics illustrate how a cetacean may integrate sensory modalities to form an adaptive foraging landscape as a function of distance to prey. The scale‐of‐senses schematic is flexible, allowing for case‐specific application and enhancement with improved cetacean sensory data. The framework serves to improve our understanding of functional cetacean foraging ecology, and to develop new hypotheses, methods, and results regarding how cetaceans forage at multiple scales.     

Sensory Perception: Lessons from Synesthesia: Using Synesthesia to Inform the Understanding of Sensory Perception

Synesthesia, the conscious, idiosyncratic, repeatable, and involuntary sensation of one sensory modality in response to another, is a condition that has puzzled both researchers and philosophers for centuries. Much time has been spent proving the condition’s existence as well as investigating its etiology, but what can be learned from synesthesia remains a poorly discussed topic. Here, synaesthesia is presented as a possible answer rather than a question to the current gaps in our understanding of sensory perception. By first appreciating the similarities between normal sensory perception and synesthesia, one can use what is known about synaesthesia, from behavioral and imaging studies, to inform our understanding of “normal” sensory perception. In particular, in considering synesthesia, one can better understand how and where the different sensory modalities interact in the brain, how different sensory modalities can interact without confusion ― the binding problem ― as well as how sensory perception develops.     

Evolutionary optimization and neural network models of behavior

One of the main challenges to the adaptionist program in general and the use of optimization models in behavioral and evolutionary ecology, in particular, is that organisms are so constrained' by ontogeny and phylogeny that they may not be able to attain optimal solutions, however those are defined. This paper responds to the challenge through the comparison of optimality and neural network models for the behavior of an individual polychaete worm. The evolutionary optimization model is used to compute behaviors (movement in and out of a tube) that maximize a measure of Darwinian fitness based on individual survival and reproduction. The neural network involves motor, sensory, energetic reserve and clock neuronal groups. Ontogeny of the neural network is the change of connections of a single individual in response to its experiences in the environment. Evolution of the neural network is the natural selection of initial values of connections between groups and learning rules for changing connections. Taken together, these can be viewed as design parameters. The best neural networks have fitnesses between 85% and 99% of the fitness of the evolutionary optimization model. More complicated models for polychaete worms are discussed. Formulation of a neural network model for host acceptance decisions by tephritid fruit flies leads to predictions about the neurobiology of the flies. The general conclusion is that neural networks appear to be sufficiently rich and plastic that even weak evolution of design parameters may be sufficient for organisms to achieve behaviors that give fitnesses close to the evolutionary optimal fitness, particularly if the behaviors are relatively simple.     

Sensory bias as an explanation for the evolution of mate preferences

The sensory bias model of sexual selection posits that female mating preferences are by-products of natural selection on sensory systems. Although sensory bias was proposed 20 years ago, its critical assumptions remain untested. This paradox arises because sensory bias has been used to explain two different phenomena. First, it has been used as a hypothesis about signal design, that is, that males evolve traits that stimulate female sensory systems. Second, sensory bias has been used as a hypothesis for the evolution of female preference itself, that is, to explain why females exhibit particular preferences. We focus on this second facet. First, we clarify the unique features of sensory bias relative to the alternative models by considering each in the same quantitative genetic framework. The key assumptions of sensory bias are that natural selection is the predominant evolutionary mechanism that affects preference and that sexual selection on preferences is quantitatively negligible. We describe four studies that would test these assumptions and review what we can and cannot infer about sensory bias from existing studies. We suggest that the importance of sensory bias as an explanation for the evolution of female preferences remains to be determined.     

Colour spaces in ecology and evolutionary biology

The recognition that animals sense the world in a different way than we do has unlocked important lines of research in ecology and evolutionary biology. In practice, the subjective study of natural stimuli has been permitted by perceptual spaces, which are graphical models of how stimuli are perceived by a given animal. Because colour vision is arguably the best‐known sensory modality in most animals, a diversity of colour spaces are now available to visual ecologists, ranging from generalist and basic models allowing rough but robust predictions on colour perception, to species‐specific, more complex models giving accurate but context‐dependent predictions. Selecting among these models is most often influenced by historical contingencies that have associated models to specific questions and organisms; however, these associations are not always optimal. The aim of this review is to provide visual ecologists with a critical perspective on how models of colour space are built, how well they perform and where their main limitations are with regard to their most frequent uses in ecology and evolutionary biology. We propose a classification of models based on their complexity, defined as whether and how they model the mechanisms of chromatic adaptation and receptor opponency, the nonlinear association between the stimulus and its perception, and whether or not models have been fitted to experimental data. Then, we review the effect of modelling these mechanisms on predictions of colour detection and discrimination, colour conspicuousness, colour diversity and diversification, and for comparing the perception of colour traits between distinct perceivers. While a few rules emerge (e.g. opponent log–linear models should be preferred when analysing very distinct colours), in general model parameters still have poorly known effects. Colour spaces have nonetheless permitted significant advances in ecology and evolutionary biology, and more progress is expected if ecologists compare results between models and perform behavioural experiments more routinely. Such an approach would further contribute to a better understanding of colour vision and its links to the behavioural ecology of animals. While visual ecology is essentially a transfer of knowledge from visual sciences to evolutionary ecology, we hope that the discipline will benefit both fields more evenly in the future.     

Applying population and community ecology theory to advance understanding of belowground biogeochemistry

Approaches to quantifying and predicting soil biogeochemical cycles mostly consider microbial biomass and community composition as products of the abiotic environment. Current numerical approaches then primarily emphasise the importance of microbe–environment interactions and physiology as controls on biogeochemical cycles. Decidedly less attention has been paid to understanding control exerted by community dynamics and biotic interactions. Yet a rich literature of theoretical and empirical contributions highlights the importance of considering how variation in microbial population ecology, especially biotic interactions, is related to variation in key biogeochemical processes like soil carbon formation. We demonstrate how a population and community ecology perspective can be used to (1) understand the impact of microbial communities on biogeochemical cycles and (2) reframe current theory and models to include more detailed microbial ecology. Through a series of simulations we illustrate how density dependence and key biotic interactions, such as competition and predation, can determine the degree to which microbes regulate soil biogeochemical cycles. The ecological perspective and model simulations we present lay the foundation for developing empirical research and complementary models that explore the diversity of ecological mechanisms that operate in microbial communities to regulate biogeochemical processes.     

Recognition of flowers by pollinators

The flowers of angiosperm plants present us with a staggering diversity of signal designs, but how did this diversity evolve? Answering this question requires us to understand how pollinators analyze these signals with their visual and olfactory sense organs, and how the sensory systems work together with post-receptor neural wiring to produce a coherent percept of the world around them. Recent research on the dynamics with which bees store, manage and retrieve memories all have fundamental implications for how pollinators choose between flowers, and in turn for floral evolution. New findings regarding how attention, peak-shift phenomena, and speed-accuracy tradeoffs affect pollinator choice between flower species show that analyzing the evolutionary ecology of signal-receiver relationships can substantially benefit from knowledge about the neural mechanisms of visual and olfactory information processing.     

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.     

Neural processing as causal inference

Perception is about making sense, that is, understanding what events in the outside world caused the sensory observations. Consistent with this intuition, many aspects of human behavior confronting noise and ambiguity are well explained by principles of causal inference. Extending these insights, recent studies have applied the same powerful set of tools to perceptual processing at the neural level. According to these approaches, microscopic neural structures solve elementary probabilistic tasks and can be combined to construct hierarchical predictive models of the sensory input. This framework suggests that variability in neural responses reflects the inherent uncertainty associated with sensory interpretations and that sensory neurons are active predictors rather than passive filters of their inputs. Causal inference can account parsimoniously and quantitatively for non-linear dynamical properties in single synapses, single neurons and sensory receptive fields.     

Mimicry for all modalities

Mimicry is a canonical example of adaptive signal design. In principle, what constitutes mimicry is independent of the taxonomic identity of the mimic, the ecological context in which it operates, and the sensory modality through which it is expressed. However, in practice the study of mimicry is inconsistent across research fields, with theoretical and empirical advances often failing to cross taxonomic and sensory divides. We propose a novel conceptual framework whereby mimicry evolves if a receiver perceives the similarity between a mimic and a model and as a result confers a selective benefit onto the mimic. Here, misidentification and/or deception are no longer formal requirements, and mimicry can evolve irrespective of the underlying proximate mechanisms. The centrality of receiver perception in this framework enables us to formally distinguish mimicry from perceptual exploitation and integrate mimicry and multicomponent signalling theory for the first time. In addition, it resolves inconsistencies in our understanding of the role of learning in mimicry evolution, and shows that imperfect mimicry is expected to be the norm. Mimicry remains a key model for understanding signal evolution and cognition, and we recommend the adoption of a unified approach to stimulate future interdisciplinary developments in this fascinating area of research.     

Parent-offspring conflict and co-adaptation: behavioural ecology meets quantitative genetics

The evolution of the complex and dynamic behavioural interactions between caring parents and their dependent offspring is a major area of research in behavioural ecology and quantitative genetics. While behavioural ecologists examine the evolution of interactions between parents and offspring in the light of parent-offspring conflict and its resolution, quantitative geneticists explore the evolution of such interactions in the light of parent-offspring co-adaptation due to combined effects of parental and offspring behaviours on fitness. To date, there is little interaction or integration between these two fields. Here, we first review the merits and limitations of each of these two approaches and show that they provide important complementary insights into the evolution of strategies for offspring begging and parental resource provisioning. We then outline how central ideas from behavioural ecology and quantitative genetics can be combined within a framework based on the concept of behavioural reaction norms, which provides a common basis for behavioural ecologists and quantitative geneticists to study the evolution of parent-offspring interactions. Finally, we discuss how the behavioural reaction norm approach can be used to advance our understanding of parent-offspring conflict by combining information about the genetic basis of traits from quantitative genetics with key insights regarding the adaptive function and dynamic nature of parental and offspring behaviours from behavioural ecology.     

Neutral Models of Microbiome Evolution

There has been an explosion of research on host-associated microbial communities (i.e.,microbiomes). Much of this research has focused on surveys of microbial diversities across a variety of host species, including humans, with a view to understanding how these microbiomes are distributed across space and time, and how they correlate with host health, disease, phenotype, physiology and ecology. Fewer studies have focused on how these microbiomes may have evolved. In this paper, we develop an agent-based framework to study the dynamics of microbiome evolution. Our framework incorporates neutral models of how hosts acquire their microbiomes, and how the environmental microbial community that is available to the hosts is assembled. Most importantly, our framework also incorporates a Wright-Fisher genealogical model of hosts, so that the dynamics of microbiome evolution is studied on an evolutionary timescale. Our results indicate that the extent of parental contribution to microbial availability from one generation to the next significantly impacts the diversity of microbiomes: the greater the parental contribution, the less diverse the microbiomes. In contrast, even when there is only a very small contribution from a constant environmental pool, microbial communities can remain highly diverse. Finally, we show that our models may be used to construct hypotheses about the types of processes that operate to assemble microbiomes over evolutionary time.     

Integrating animal temperament within ecology and evolution

Temperament describes the idea that individual behavioural differences are repeatable over time and across situations. This common phenomenon covers numerous traits, such as aggressiveness, avoidance of novelty, willingness to take risks, exploration, and sociality. The study of temperament is central to animal psychology, behavioural genetics, pharmacology, and animal husbandry, but relatively few studies have examined the ecology and evolution of temperament traits. This situation is surprising, given that temperament is likely to exert an important influence on many aspects of animal ecology and evolution, and that individual variation in temperament appears to be pervasive amongst animal species. Possible explanations for this neglect of temperament include a perceived irrelevance, an insufficient understanding of the link between temperament traits and fitness, and a lack of coherence in terminology with similar traits often given different names, or different traits given the same name. We propose that temperament can and should be studied within an evolutionary ecology framework and provide a terminology that could be used as a working tool for ecological studies of temperament. Our terminology includes five major temperament trait categories: shyness-boldness, exploration-avoidance, activity, sociability and aggressiveness. This terminology does not make inferences regarding underlying dispositions or psychological processes, which may have restrained ecologists and evolutionary biologists from working on these traits. We present extensive literature reviews that demonstrate that temperament traits are heritable, and linked to fitness and to several other traits of importance to ecology and evolution. Furthermore, we describe ecologically relevant measurement methods and point to several ecological and evolutionary topics that would benefit from considering temperament, such as phenotypic plasticity, conservation biology, population sampling, and invasion biology.     

Selection on quantitative colour variation in Centaurea cyanus: the role of the pollinator's visual system

Even though the importance of selection for trait evolution is well established, we still lack a functional understanding of the mechanisms underlying phenotypic selection. Because animals necessarily use their sensory system to perceive phenotypic traits, the model of sensory bias assumes that sensory systems are the main determinant of signal evolution. Yet, it has remained poorly known how sensory systems contribute to shaping the fitness surface of selected individuals. In a greenhouse experiment, we quantified the strength and direction of selection on floral coloration in a population of cornflowers exposed to bumblebees as unique pollinators during 4 days. We detected significant selection on the chromatic and achromatic (brightness) components of floral coloration. We then studied whether these patterns of selection are explicable by accounting for the visual system of the pollinators. Using data on bumblebee colour vision, we first showed that bumblebees should discriminate among quantitative colour variants. The observed selection was then compared to the selection predicted by psychophysical models of bumblebee colour vision. The achromatic but not the chromatic channel of the bumblebee's visual system could explain the observed pattern of selection. These results highlight that (i) pollinators can select quantitative variation in floral coloration and could thus account for a gradual evolution of flower coloration, and (ii) stimulation of the visual system represents, at least partly, a functional mechanism potentially explaining pollinators' selection on floral colour variants.     

Motor control of birdsong

One of the challenges when considering the motor control of birdsong is to understand how such a wide variety of temporally and spectrally diverse vocalizations are learned and produced. A better understanding of central neural processing, together with direct endoscopic observations and physiological studies of peripheral motor function during singing, has resulted in the formation of new theoretical models of song production. Recent work suggests that it may be more profitable to focus on the temporal relationship between control parameters than to attempt to directly correlate neural processing with details of the acoustic output.     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

Mathematical description of gene regulatory units

Revealing the control mechanisms responsible for the cell's surprisingly well-organized functions should lead directly to a better understanding of how the cell adapts to extraordinarily changing environments. A general framework for describing models that can represent diverse biochemical regulatory functions systematically would help not only systematic interpretation of the various models proposed for certain systems but also further understanding of the general control mechanism and design principles underlying different biological systems. This article presents a unified mathematical framework for describing gene regulatory units. The proposed framework is fairly compatible with the classical control theoretical framework, so it should serve as a connecting bridge between engineering control theory and biological control mechanisms. It should also provide a unified view of different regulatory units and facilitate systematic comparison of different mathematical models proposed in a variety of literature.     

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.     

Finding the way with a noisy brain

Successful navigation is fundamental to the survival of nearly every animal on earth, and achieved by nervous systems of vastly different sizes and characteristics. Yet surprisingly little is known of the detailed neural circuitry from any species which can accurately represent space for navigation. Path integration is one of the oldest and most ubiquitous navigation strategies in the animal kingdom. Despite a plethora of computational models, from equational to neural network form, there is currently no consensus, even in principle, of how this important phenomenon occurs neurally. Recently, all path integration models were examined according to a novel, unifying classification system. Here we combine this theoretical framework with recent insights from directed walk theory, and develop an intuitive yet mathematically rigorous proof that only one class of neural representation of space can tolerate noise during path integration. This result suggests many existing models of path integration are not biologically plausible due to their intolerance to noise. This surprising result imposes significant computational limitations on the neurobiological spatial representation of all successfully navigating animals, irrespective of species. Indeed, noise-tolerance may be an important functional constraint on the evolution of neuroarchitectural plans in the animal kingdom.     

一种泡蟾的配偶识别:脑、行为和进化研究

综述了对泡蟾(Physalaemuspus pustulosus)的性通讯所进行的各种研究。本文作者研究了通讯系统的功能重要性、通讯的神经机制以及通讯进化的格局,特别研究了雌蛙如何使用简单和复杂的叫声来识别可以交配的物种,以及如何识别同种的不同雄性个体。本文确定了复杂叫声的反选择力、一种食蛙蝠——缨唇蝠(Trachops cirrhosus)以及叫声的能量消耗,并阐述了引起雌性反应的重要特征。作者的比较研究表明,对复杂叫声的偏好在复杂叫声进化前就有了,这支持感官开发假说(Hypothesis of sensory exploitation,雄性特征进化的结果是开发雌性预先存在的对雄性的偏好)。结合比较研究和人工神经网络的模拟,作者认为雌蛙用来识别雄性简单叫声的解码策略受其祖先解码策略的影响[动物学报49(6)：713～726,2003]。