How does Behavior Change the Brain? Multiple Methods to Answer Old Questions

Clearly the brain controls behavior but can behavior also "control" the brain? On an evolutionary time scale, selective ecological pressures shape the sensory and motor capacities as well as the body and behavior. Correspondingly, in development, behavior acts in concert with the environment to cause structural changes in the brain lasting a lifetime. Surprisingly, in "real time" social behavior can also cause changes, typically reversible, in the brain in adult animals. Changes caused by behavioral interactions can be dramatic, and in many instances, these interactions are directly related to reproductive behavior. Understanding how behavior sculpts the brain in the course of behavioral interactions is a major challenge. Analyzing such changes requires a model system allowing control of the biological and behavioral environment of many animals simultaneously yet allowing access to physiological, cellular and molecular processes being regulated. The mouthbrooding cichlid Haplochromis (Astatotilapia) burtoni (Günther) from Lake Tanganyika lends itself to the study of social influences on the brain. It has complex, though easily observable individual and social behaviors regulated by two distinct classes of males, those with territories and those without. Many features of the animals are shaped by social encounters including the maturation of juveniles, the hypothalamic-pituitary-gonadal axis, the growth rate, the basal stress level among others. How does social information effect change in the brain and body? Animals must attend to the social scene to identify their chances. Learning how social information is transduced into cellular changes in this species should help understand how this happens in other social animals.     

Collective behavior in relation with changing environments: Dynamics,modularity, and agency

Collective behavior operates without central control, using local interactions among participants to adjust to changing conditions. Many natural systems operate collectively, and by specifying what objectives are met by the system, the idea of agency helps to describe how collective behavior is embedded in the conditions it deals with. Ant colonies function collectively, and the enormous diversity of more than 15K species of ants, in different habitats, provides opportunities to look for general ecological patterns in how collective behavior operates. The foraging behavior of harvester ants in the desert regulates activity to manage water loss, while the trail networks of turtle ants in the canopy tropical forest respond to rapidly changing resources and vegetation. These examples illustrate some broad correspondences in natural systems between the dynamics of collective behavior and the dynamics of the surroundings. To outline how interactions among participants, acting in relation with changing surroundings, achieve collective outcomes, I focus on three aspects of collective behavior: the rate at which interactions adjust to conditions, the feedback regime that stimulates and inhibits activity, and the modularity of the network of interactions. To characterize the dynamics of the surroundings, I consider gradients in stability, energy flow, and the distribution of resources and demands. I then propose some hypotheses that link how collective behavior operates with changing environments.     

Hormonal and non-hormonal bases of maternal behavior: The role of experience and epigenetic mechanisms

This article is part of a Special Issue “Parental Care”. Though hormonal changes occurring throughout pregnancy and at the time of parturition have been demonstrated to prime the maternal brain and trigger the onset of mother–infant interactions, extended experience with neonates can induce similar behavioral interactions. Sensitization, a phenomenon in which rodents engage in parental responses to young following constant cohabitation with donor pups, was elegantly demonstrated by Rosenblatt (1967) to occur in females and males, independent of hormonal status. Study of the non-hormonal basis of maternal behavior has contributed significantly to our understanding of hormonal influences on the maternal brain and the cellular and molecular mechanisms that mediate maternal behavior. Here, we highlight our current understanding regarding both hormone-induced and experience-induced maternal responsivity and the mechanisms that may serve as a common pathway through which increases in maternal behavior are achieved. In particular, we describe the epigenetic changes that contribute to chromatin remodeling and how these molecular mechanisms may influence the neural substrates of the maternal brain. We also consider how individual differences in these systems emerge during development in response to maternal care. This research has broad implications for our understanding of the parental brain and the role of experience in the induction of neurobiological and behavior changes.     

“It <Emphasis Type="Italic">Becomes</Emphasis> Scientific…:” Carbon Accounting for REDD+ in Malawi

I present a case study of project planning meetings during which meticulous accounting procedures are used to convert social and ecological life into marketable carbon credits. This focus on the micro-processes of carbon credit production nuances the understanding of: (1) the politics involved in these calculations in small-scale interactions; (2) the particular mechanisms through which social and ecological life is made equivalent to carbon units, social actions, and other places across the globe; (3) how these equivalencies and quantifications co-constitute one another; and (4) how this process (re)imbues these objective numbers with a particular context that is generated from the nexus of local and cosmopolitan expertise. This process lends legitimacy to an otherwise imprecise set of accounting practices and translates social and ecological data into forms legible to transnational commodity markets and investors.     

Forbidden versus permitted interactions: Disentangling processes from patterns in ecological network analysis

Several studies have identified the tendency for species to share interacting partners as a key property to the functioning and stability of ecological networks. However, assessing this pattern has proved challenging in several regards, such as finding proper metrics to assess node overlap (sharing), and using robust null modeling to disentangle significance from randomness. Here, we bring attention to an additional, largely neglected challenge in assessing species’ tendency to share interacting partners. In particular, we discuss and illustrate with two different case studies how identifying the set of “permitted” interactions for a given species (i.e. interactions that are not impeded, e.g. by lack of functional trait compatibility) is paramount to understand the ecological and co‐evolutionary processes at the basis of node overlap and segregation patterns.     

The behavioural ecology of marine cleaning mutualisms

Cleaning interactions, in which a small ‘cleaner’ organism removes and often consumes material from a larger ‘client’, are some of the most enigmatic and intriguing of interspecies interactions. Early research on cleaning interactions canonized the view that they are mutualistic, with clients benefiting from parasite removal and cleaners benefiting from a meal, but subsequent decades of research have revealed that the dynamics of these interactions can be highly complex. Despite decades of research on marine cleaning interactions (the best studied cleaning systems), key questions remain, including how the outcome of an individual cleaning interaction depends on ecological, behavioural, and social context, how such interactions arise, and how they remain stable over time. Recently, studies of marine parasites, long-term data from coral reef communities with and without cleaners, increased behavioural observations recorded using remote video, and a focus on a larger numbers of cleaning species have helped bring about key conceptual advances in our understanding of cleaning interactions. In particular, evidence now suggests that the ecological, behavioural, and social contexts of a given cleaning interaction can result in the outcome ranging from mutualistic to parasitic, and that cleaning interactions are mediated by signals that can also vary with context. Signals are an important means by which animals extract information about one another, and thus represent a mechanism by which interspecific partners can determine when, how, and with whom to interact. Here, I review our understanding of the behavioural ecology of marine cleaning interactions. In particular, I argue that signals provide a useful framework for advancing our understanding of several important outstanding questions. I discuss the costs and benefits of cleaning interactions, review how cleaners and clients recognize and assess one another using signals, and discuss how signal reliability, or ‘honesty’, may be maintained in cleaning systems. Lastly, I discuss the sensory ecology of both cleaners and clients to highlight what marine cleaning systems can tell us about signalling behaviour, signal form, and signal evolution in a system where signals are aimed at multiple receiver species. Overall, I argue that future research on cleaning interactions has much to gain by continuing to shift the research focus toward examining the variable outcomes of cleaning interactions in relation to the broader behavioural, social, and ecological contexts.     

PATHWAYS TO SOCIAL EVOLUTION: RECIPROCITY,RELATEDNESS, AND SYNERGY

Many organisms live in populations structured by space and by class, exhibit plastic responses to their social partners, and are subject to nonadditive ecological and fitness effects. Social evolution theory has long recognized that all of these factors can lead to different selection pressures but has only recently attempted to synthesize how these factors interact. Using models for both discrete and continuous phenotypes, we show that analyzing these factors in a consistent framework reveals that they interact with one another in ways previously overlooked. Specifically, behavioral responses (reciprocity), genetic relatedness, and synergy interact in nontrivial ways that cannot be easily captured by simple summary indices of assortment. We demonstrate the importance of these interactions by showing how they have been neglected in previous synthetic models of social behavior both within and between species. These interactions also affect the level of behavioral responses that can evolve in the long run; proximate biological mechanisms are evolutionarily stable when they generate enough responsiveness relative to the level of responsiveness that exactly balances the ecological costs and benefits. Given the richness of social behavior across taxa, these interactions should be a boon for empirical research as they are likely crucial for describing the complex relationship linking ecology, demography, and social behavior.     

Cell death and the song control system: a model for how sex steroid hormones regulate naturally-occurring neurodegeneration

The production, learning, and perception of song in songbirds are regulated by a series of discrete brain nuclei known as the song control system. In most songbird species, the song control system is sexually dimorphic, and these dimorphisms become more robust after birds have hatched. In seasonally breeding songbirds, the song control system grows and regresses depending upon breeding context. The development and seasonal plasticity of the song control system are dependent upon neurodegenerative processes, which can be ameliorated, at least in part, by circulating sex steroid hormones. I will describe two areas of song control system research that have provided important information about how hormonal control of cell death contributes to the shaping of behaviorally-relevant brain circuits. First, sexual dimorphism in the zebra finch song control system is robust and emerges partially due to substantial regression of female song control system nuclei during development. Second, in seasonally-breeding songbirds, the song control system regresses as birds transition from breeding to non-breeding conditions. In a controlled laboratory setting where hormones can be acutely withdrawn, these brain areas regress in only a matter of hours to days. Taken together, these results demonstrate that the study of cell death in the song control system provides an excellent opportunity for understanding how changes in circulating levels of sex steroids affect the degeneration of hormone-sensitive brain circuits.     

结构稳定性: 概念、方法和应用

群落内物种间相互作用的结构是高度组织化的。群落结构对多物种共存的影响机制是群落生态学的核心科学问题之一。目前生态学界在这一问题上存在多种不同的观点。一个可能的原因是, 由于环境因子的复杂性, 大部分研究忽略了环境因子对群落结构和物种共存的重要影响。在这一背景下, 近期发展起来的结构稳定性理论系统地联系了群落结构、环境因子和物种共存, 并在此基础上建立了一个和经验数据紧密结合的理论框架。本文首先简要回顾了当前关于群落结构研究的争鸣, 然后介绍了结构稳定性的理论框架和计算方法, 最后详细介绍了结构稳定性理论在不同生态群落和不同生态学问题中的应用。在全球气候变化的背景下, 结构稳定性理论提供了一种新的视角来理解群落层面的生物多样性维持机制。     

Topology and robustness in the Drosophila segment polarity network

A complex hierarchy of genetic interactions converts a single-celled Drosophila melanogaster egg into a multicellular embryo with 14 segments. Previously, von Dassow et al. reported that a mathematical model of the genetic interactions that defined the polarity of segments (the segment polarity network) was robust (von Dassow et al. 2000). As quantitative information about the system was unavailable, parameters were sampled randomly. A surprisingly large fraction of these parameter sets allowed the model to maintain and elaborate on the segment polarity pattern. This robustness is due to the positive feedback of gene products on their own expression, which induces individual cells in a model segment to adopt different stable expression states (bistability) corresponding to different cell types in the segment polarity pattern. A positive feedback loop will only yield multiple stable states when the parameters that describe it satisfy a particular inequality. By testing which random parameter sets satisfy these inequalities, I show that bistability is necessary to form the segment polarity pattern and serves as a strong predictor of which parameter sets will succeed in forming the pattern. Although the original model was robust to parameter variation, it could not reproduce the observed effects of cell division on the pattern of gene expression. I present a modified version that incorporates recent experimental evidence and does successfully mimic the consequences of cell division. The behavior of this modified model can also be understood in terms of bistability in positive feedback of gene expression. I discuss how this topological property of networks provides robust pattern formation and how large changes in parameters can change the specific pattern produced by a network.     

Describing and understanding behavioral responses to multiple stressors and multiple stimuli

Understanding the effects of environmental change on natural ecosystems is a major challenge, particularly when multiple stressors interact to produce unexpected “ecological surprises” in the form of complex, nonadditive effects that can amplify or reduce their individual effects. Animals often respond behaviorally to environmental change, and multiple stressors can have both population‐level and community‐level effects. However, the individual, not combined, effects of stressors on animal behavior are commonly studied. There is a need to understand how animals respond to the more complex combinations of stressors that occur in nature, which requires a systematic and rigorous approach to quantify the various potential behavioral responses to the independent and interactive effects of stressors. We illustrate a robust, systematic approach for understanding behavioral responses to multiple stressors based on integrating schemes used to quantitatively classify interactions in multiple‐stressor research and to qualitatively view interactions between multiple stimuli in behavioral experiments. We introduce and unify the two frameworks, highlighting their conceptual and methodological similarities, and use four case studies to demonstrate how this unification could improve our interpretation of interactions in behavioral experiments and guide efforts to manage the effects of multiple stressors. Our unified approach: (1) provides behavioral ecologists with a more rigorous and systematic way to quantify how animals respond to interactions between multiple stimuli, an important theoretical advance, (2) helps us better understand how animals behave when they encounter multiple, potentially interacting stressors, and (3) contributes more generally to the understanding of “ecological surprises” in multiple stressors research.     

Puma population limitation and regulation: What matters in puma management?

Wildlife managers require reliable information on factors that influence animal populations to develop successful management programs, including the puma (Puma concolor), in western North America. As puma populations have recovered in recent decades because of restrictions on human-caused mortality, managers need a clear understanding of the factors that limit or regulate puma populations and how those factors might be manipulated to achieve management objectives, including sustaining puma and other wildlife populations, providing hunting opportunity, and reducing puma interactions with people. I synthesized technical literature on puma populations, behavior, and relationships with prey that have contributed to hypotheses on puma population limitation and regulation. Current hypotheses on puma population limitation include the social limitation hypothesis and the food limitation hypothesis. Associated with each of those are 2 hypotheses on puma population regulation: the social regulation hypothesis and the competition regulation hypothesis. I organize the biological and ecological attributes of pumas reported in the literature under these hypotheses. I discuss the validity of these hypotheses based on the limits of the research associated with the hypotheses and the evolutionary processes theoretically underlying them. I review the management predictions as framed by these hypotheses as they pertain to puma hunting, puma-prey relationships, and human-puma interactions. The food limitation and competition regulation hypotheses explain more phenomena associated with puma and likely would guide more successful management outcomes. © 2019 The Wildlife Society.     

Analysing ecological networks of species interactions

Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems – such as communities – through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with – what we believe to be – their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.     

Ecological frames of mind: the role of cognition in behavioral ecology.

Cognitive psychology is the study of how information, from the senses and from memory, is used in the production of behavior. Investigation of the specifics of behavioral adaptation has already led some behavioral ecologists into the domain of animal cognition. I make several arguments for the benefits and the necessity of a sophisticated assessment by ecologists of the cognitive aspects of behavioral adaptation. First, because cognition typically serves to produce adaptive behavior, cognitive structure and function should reflect ecological demands; studies of cognition in ecological contexts are opportunities to understand adaptation. Furthermore, constraints on cognitive properties may help determine how behavior meets the environment. Studies of spatial memory in food-caching corvids exemplify how cognitive aspects of behavior may both reflect and determine specifics of adaptation. Second, many models in behavioral ecology assume certain cognitive abilities, such as timing or counting. Cognitive theory and methodology should be used to determine whether animals possess these abilities. I have provided examples. Third, consideration of cognitive function can lead to original ideas about the details of behavioral adaptation. Without a thorough integration of cognitive psychology with behavioral ecology, our understanding of the relation between behavior and selective pressures will be compromised.     

When does reproductive interference occur? Predictions and data

Reproductive interference is interspecific sexual interactions that are costly to at least one species involved. Although many studies have reported a substantial fitness cost associated with reproductive interference, suggesting its ecological significance, others have not observed reproductive interference in study species. Reproductive interference that incurs a large fitness cost is more likely to occur during secondary contacts than between long-coexisting species. I first explain the rationale underlying this prediction using existing literature. Next, I present a conceptual framework to classify pairs of interacting species into one of four states, defined by the ecological and evolutionary stabilities of the species pairs. I discuss how the stability states of species pairs are likely to change over time, along with changes in the demographic and evolutionary role of reproductive interference. I then perform literature survey to test the prediction that reproductive interference should be more prevalent in secondary contact. Finally, I discuss the implications of the proposed conceptual framework and literature survey result.     

Mechanosensitive body–brain interactions in Caenorhabditis elegans

Proprioception and visceral mechanosensation provide important information about the location and deformation of the body parts in space and time. These deformations arise from muscle contraction during locomotion, but also from volume changes in organs that are subjected to stresses as a part of their physiological function. These internal morphodynamics give rise to periodic contraction–relaxation cycles with surprisingly constant amplitudes and the maintenance of these optimal driving patterns is remarkably robust against external and internal perturbations. One of the underlying reason for this robustness is an internal feedback mechanism in which specialized sensory cells and neurons signal the mechanical deformation of the inner workings of our organs, from the body to the brain, which subsequently adjust the driver to a predetermined physiological setpoint. Here, we review recent progress in the field of visceral mechanosensation and proprioception in Caenorhabditis elegans and discuss how future studies with this model can be used to gain insight into mechanosensory body–brain interactions in mammals.     

Increasing awareness of avian ecological function

Birds are one of the most diverse groups of ecosystem service providers, whose ecological functions range from creating soil to shaping primate behavior, Nevertheless, the impression that birds have little influence on ecological processes has been hard to change. Given the ongoing declines in avian functional groups, there is a pressing need to compare avian ecological functions to those of other taxa, to understand how these functions translate to ecosystem services and to estimate the ecological implications of bird declines. Here, I review the ecological functions of birds, link them to ecosystem services and outline research priorities for understanding avian contributions to ecosystem functioning.     

Predation- and competition-mediated brain plasticity in Rana temporaria tadpoles

An increasing number of studies have demonstrated phenotypic plasticity in brain size and architecture in response to environmental variation. However, our knowledge on how brain architecture is affected by commonplace ecological interactions is rudimentary. For example, while intraspecific competition and risk of predation are known to induce adaptive plastic modifications in morphology and behaviour in a wide variety of organisms, their effects on brain development have not been studied. We studied experimentally the influence of density and predation risk on brain development in common frog (Rana temporaria) tadpoles. Tadpoles grown at low density and under predation risk developed smaller brains than tadpoles at the other treatment combinations. Further, at high densities, tadpoles developed larger optic tecta and smaller medulla oblongata than those grown at low densities. These results demonstrate that ecological interactions - like intraspecific competition and predation risk - can have strong effects on brain development in lower vertebrates.     

Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.