From mechanisms to function: an integrated framework of animal innovation

Animal innovations range from the discovery of novel food types to the invention of completely novel behaviours. Innovations can give access to new opportunities, and thus enable innovating agents to invade and create novel niches. This in turn can pave the way for morphological adaptation and adaptive radiation. The mechanisms that make innovations possible are probably as diverse as the innovations themselves. So too are their evolutionary consequences. Perhaps because of this diversity, we lack a unifying framework that links mechanism to function. We propose a framework for animal innovation that describes the interactions between mechanism, fitness benefit and evolutionary significance, and which suggests an expanded range of experimental approaches. In doing so, we split innovation into factors (components and phases) that can be manipulated systematically, and which can be investigated both experimentally and with correlational studies. We apply this framework to a selection of cases, showing how it helps us ask more precise questions and design more revealing experiments.     

An ecoclimatic framework for evaluating the resilience of vegetation to water deficit

The surge in global efforts to understand the causes and consequences of drought on forest ecosystems has tended to focus on specific impacts such as mortality. We propose an ecoclimatic framework that takes a broader view of the ecological relevance of water deficits, linking elements of exposure and resilience to cumulative impacts on a range of ecosystem processes. This ecoclimatic framework is underpinned by two hypotheses: (i) exposure to water deficit can be represented probabilistically and used to estimate exposure thresholds across different vegetation types or ecosystems; and (ii) the cumulative impact of a series of water deficit events is defined by attributes governing the resistance and recovery of the affected processes. We present case studies comprising Pinus edulis and Eucalyptus globulus, tree species with contrasting ecological strategies, which demonstrate how links between exposure and resilience can be examined within our proposed framework. These examples reveal how climatic thresholds can be defined along a continuum of vegetation functional responses to water deficit regimes. The strength of this framework lies in identifying climatic thresholds on vegetation function in the absence of more complete mechanistic understanding, thereby guiding the formulation, application and benchmarking of more detailed modelling.     

What Does It Mean to Be Musical?

Music can be seen as a model system for understanding gene × environment interactions and how these can influence neurocognitive development. The concept of musicality, however, is underspecified and not well understood. Here, I propose a framework for defining musicality to provide a foundation for studying the contributions of biological and environmental factors.     

基于“源-汇”生态过程的景观格局识别方法——景观空间负荷对比指数

正确理解景观格局与生态过程的关系是进一步深化景观生态学研究的关键,但是由于景观格局和生态过程涉及到不同的研究尺度,并且随着尺度的变化而变化,加上面状生态过程监测数据无法直接获得,导致很难定量描述景观格局与生态过程之间的关系。本文以非点源污染作为研究的典型生态过程,通过分析不同景观类型及其空间分布格局在非点源污染形成过程中的地位和作用,借用洛伦兹曲线的理论,从距离、相对高度和坡度三个方面建立了不受尺度限制的景观格局评价模型——景观空间负荷对比指数。该指数可以较好地将具有面状特性的景观格局与点状监测数据有机地结合在一起,使定量研究流域景观格局与生态过程的关系成为可能。该评价模型的特点有以下几个：①不受空间尺度的限制,具有跨尺度的功能;②适宜于环境背景(降雨和土壤等)相似的地区;③景观空间负荷对比指数具有相对比较意义,其值越大,表示该类景观空间格局对流域出口监测点的贡献越大,反之越小;④景观空间负荷对比指数不能用来预测流域出口监测点非点源污染或水土流失的值,但是可以通过比较计算不同流域景观空间负荷对比指数,来判断流域发生养分(水土)流失的危险性。     

Jamming and arrest of cell motion in biological tissues

Collective cell motility is crucial to many biological processes including morphogenesis, wound healing, and cancer invasion. Recently, the biology and biophysics communities have begun to use the term ‘cell jamming’ to describe the collective arrest of cell motion in tissues. Although this term is widely used, the underlying mechanisms are varied. In this review, we highlight three independent mechanisms that can potentially drive arrest of cell motion — crowding, tension-driven rigidity, and reduction of fluctuations — and propose a framework that connects all three. Because multiple mechanisms may be operating simultaneously, this emphasizes that experiments should strive to identify which mechanism dominates in a given situation. We also discuss how specific cell-scale and molecular-scale biological processes, such as cell–cell and cell-substrate interactions, control aspects of these underlying physical mechanisms.     

A hierarchical deductive approach for functional types in disturbed ecosystems

Abstract. We propose a hierarchical approach for plant functional classification in disturbed ecosystems to be used for vegetation modelling and global plant trait comparisons. Our framework is based on the persistence of plants at different levels of organization. We assume that the main parameters to determine persistence in chronically disturbed ecosystems are those related to: I ndividual‐persistence capacity, P ropagule‐persistence capacity (persistence at the population level), C ompetitive capacity (persistence at the community level) and D ispersal capacity (persistence at the landscape level). The IPCD approach is illustrated for fire‐prone and grazed ecosystems from the Mediterranean region and Australia and by assuming a binary classification of the four traits determining persistence which give a total 16 possible functional types. The IPCD framework provides a simple structured and synthetic view from which more elaborated schemes can be developed.     

ON THE PARADIGM OF ALTRUISTIC SUICIDE IN THE UNICELLULAR WORLD

Altruistic suicide is best known in the context of programmed cell death (PCD) in multicellular individuals, which is understood as an adaptive process that contributes to the development and functionality of the organism. After the realization that PCD‐like processes can also be induced in single‐celled lineages, the paradigm of altruistic cell death has been extended to include these active cell death processes in unicellular organisms. Here, we critically evaluate the current conceptual framework and the experimental data used to support the notion of altruistic suicide in unicellular lineages, and propose new perspectives. We argue that importing the paradigm of altruistic cell death from multicellular organisms to explain active death in unicellular lineages has the potential to limit the types of questions we ask, thus biasing our understanding of the nature, origin, and maintenance of this trait. We also emphasize the need to distinguish between the benefits and the adaptive role of a trait. Lastly, we provide an alternative framework that allows for the possibility that active death in single‐celled organisms is a maladaptive trait maintained as a byproduct of selection on pro‐survival functions, but that could—under conditions in which kin/group selection can act—be co‐opted into an altruistic trait.     

Towards a multi‐trophic extension of metacommunity ecology

Metacommunity theory provides an understanding of how spatial processes determine the structure and function of communities at local and regional scales. Although metacommunity theory has considered trophic dynamics in the past, it has been performed idiosyncratically with a wide selection of possible dynamics. Trophic metacommunity theory needs a synthesis of a few influential axis to simplify future predictions and tests. We propose an extension of metacommunity ecology that addresses these shortcomings by incorporating variability among trophic levels in ‘spatial use properties’. We define ‘spatial use properties’ as a set of traits (dispersal, migration, foraging and spatial information processing) that set the spatial and temporal scales of organismal movement, and thus scales of interspecific interactions. Progress towards a synthetic predictive framework can be made by (1) documenting patterns of spatial use properties in natural food webs and (2) using theory and experiments to test how trophic structure in spatial use properties affects metacommunity dynamics.     

Testing Local Independence between Two Point Processes

Dependencies between two types of points in a spatial point process can be due either to a real dependence between the two types or to the dependence on common underlying variables. We propose a global test for dependence between two point processes that is valid for a wide range of models. In contrast with previously proposed methods, it is based on a number of local test statistics, which makes it possible to map the local association between the two processes. The behavior of the test is evaluated by a simulation study. It is then applied to a vegetation pattern data set from Burkina Faso.     

Integrating selection,niche, and diversification into a hierarchical conceptual framework

Recently, new phylogenetic comparative methods have been proposed to test for the association of biological traits with diversification patterns, with species ecological “niche” being one of the most studied traits. In general, these methods implicitly assume natural selection acting at the species level, thus implying the mechanism of species selection. However, natural selection acting at the organismal level could also influence diversification patterns (i.e., effect macroevolution). Owing to our scarce knowledge on multi-level selection regarding niche as a trait, we propose a conceptual model to discuss and guide the test between species selection and effect macroevolution within a hierarchical framework. We first assume niche as an organismal as well as a species’ trait that interacts with the environment and results in species-level differential fitness. Then, we argue that niche heritability, a requirement for natural selection, can be assessed by its phylogenetic signal. Finally, we propose several predictions that can be tested in the future by disentangling both types of evolutionary processes (species selection or effect macroevolution). Our framework can have important implications for guiding analyses that aim to understand the hierarchical perspective of evolution.     

Processes underlying the effect of mycorrhizal symbiosis on plant-plant interactions

Mycorrhizal symbiosis has important implications for the diversity and productivity of plant communities. However, our mechanistic understanding of its influence on the outcome of plant-plant interactions is still expanding. In this review we propose a framework that might be useful to efficiently approach the effects of mycorrhizal fungi (MF) on plant-plant interactions. We propose several scenarios that can theoretically result in different outcomes of plant-plant interactions based on the combination of two processes: the diversity of resources provided by MF taxa to their host (resource dissimilarity) and contrasting ways of distributing those resources (resource distribution). Then, we illustrate our arguments with different ecological contexts where certain combinations of these two processes are prone to occur. The proposed framework suggests testable hypotheses that can contribute to elucidate relevant processes underlying the effects of mycorrhizal symbiosis on plant-plant interactions.     

Predicting population and community dynamics: The type of aggregation matters

When investigating complex ecological dynamics at the population or community level, we necessarily need to abstract and aggregate ecological information. The way in which information is aggregated may be crucial for the outcome of the study. In this paper, we suggest that in addition to the traditional spatial, temporal and organizational levels, we need a more flexible framework linking ecological processes, study objects and types of aggregation. We develop such a framework and exemplify the most commonly used types of aggregation and their potential influence on identifiable drivers of community dynamics. We also illustrate strategies to narrow down the range of possible aggregation types for a particular study. With this approach, we hope (i) to clarify the function of aggregation types as related to traditional ecological levels and (ii) to raise the awareness of how important a deliberate way of aggregating ecological information is for a sound and reliable outcome of any empirical or theoretical ecological study.     

Systematic Planning of Genome-Scale Experiments in Poorly Studied Species

Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.     

The tuberous sclerosis complex proteins--a GRIPP on cognition and neurodevelopment

Tuberous sclerosis complex (TSC) is a multi-system disorder associated with mutations in the TSC1 (hamartin) or TSC2 (tuberin) genes. The neurocognitive features of TSC show wide variability and have generally been attributed to structural brain abnormalities and/or seizures. We review the fundamental roles of TSC1 and TSC2 in cell signalling and propose that because the hamartin-tuberin complex (hereafter referred to as TSC1-2) acts as a global regulator and integrator of a range of physiological processes ('GRIPP') the neurocognitive manifestations of TSC result directly from cell-signalling abnormalities. Under the GRIPP hypothesis, the spectrum of neurodevelopmental abnormalities is caused by the biochemical consequences of individual TSC1 and TSC2 mutations. Recognizing the importance of signalling disruption in the brain might improve our understanding of other neurocognitive disorders.     

Genetic analysis of the Drosophila ellipsoid body neuropil: organization and development of the central complex.

The central complex is an important center for higher-order brain function in insects. It is an intricate neuropil composed of four substructures. Each substructure contains repeated neuronal elements which are connected by processes such that topography is maintained. Although the neuronal architecture has been described in several insects and the behavioral role investigated in various experiments, the exact function of this neuropil has proven elusive. To describe the architecture of the central complex, we study 15 enhancer-trap lines that label various ellipsoid body neuron types. We find evidence for restriction of gene expression that is correlated with specific neuronal types: such correlations suggest functional classifications as well. We show that some enhancer-trap patterns reveal a single ellipsoid body neuron type, while others label multiple types. We describe the development of the ellipsoid body neuropil in wild-type animals and propose developmental mechanisms based on animals displaying structural mutations of this neuropil. The experiments performed here demonstrate the degree of resolution possible from the analysis of enhancer-trap lines and form a useful library of tools for future structure/function studies of the ellipsoid body.     

Xylem embolism refilling and resilience against drought-induced mortality in woody plants: processes and trade-offs

Understanding which species are able to recover from drought, under what conditions, and the mechanistic processes involved, will facilitate predictions of plant mortality in response to global change. In response to drought, some species die because of embolism-induced hydraulic failure, whilst others are able to avoid mortality and recover, following rehydration. Several tree species have evolved strategies to avoid embolism, whereas others tolerate high embolism rates but can recover their hydraulic functioning upon drought relief. Here, we focus on structures and processes that might allow some plants to recover from drought stress via embolism reversal. We provide insights into how embolism repair may have evolved, anatomical and physiological features that facilitate this process, and describe possible trade-offs and related costs. Recent controversies on methods used for estimating embolism formation/repair are also discussed, providing some methodological suggestions. Although controversial, embolism repair processes are apparently based on the activity of phloem and ray/axial parenchyma. The mechanism is energetically demanding, and the costs to plants include metabolism and transport of soluble sugars, water and inorganic ions. We propose that embolism repair should be considered as a possible component of a ‘hydraulic efficiency-safety’ spectrum. We also advance a framework for vegetation models, describing how vulnerability curves may change in hydrodynamic model formulations for plants that recover from embolism.     

Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.     

RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction

Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using 'pseudo-energies' to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with 'soft' constraints of different strength. We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect RNA folding such as chemical nucleotide modifications and protein binding.