A unifying framework for metapopulation dynamics

Many biologically important processes, such as genetic differentiation, the spread of disease, and population stability, are affected by the (natural or enforced) subdivision of populations into networks of smaller, partly isolated, subunits. Such "metapopulations" can have extremely complex dynamics. We present a new general model that uses only two functions to capture, at the metapopulation scale, the main behavior of metapopulations. We show how complex, structured metapopulation models can be translated into our generalized framework. The metapopulation dynamics arising from some important biological processes are illustrated: the rescue effect, the Allee effect, and what we term the "antirescue effect." The antirescue effect captures instances where high migration rates are deleterious to population persistence, a phenomenon that has been largely ignored in metapopulation conservation theory. Management regimes that ignore a significant antirescue effect will be inadequate and may actually increase extinction risk. Further, consequences of territoriality and conspecific attraction on metapopulation-level dynamics are investigated. The new, simplified framework can incorporate knowledge from epidemiology, genetics, and population biology in a phenomenological way. It opens up new possibilities to identify and analyze the factors that are important for the evolution and persistence of the many spatially subdivided species.     

A unifying probabilistic framework for analyzing residual dipolar couplings

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy.     

A unifying computational framework for motor control and social interaction

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.     

A mathematical and statistical framework for modelling dispersal

Mechanistic and phenomenological dispersal modelling of organisms has long been an area of intensive research. Recently, there has been an increased interest in intermediate models between the two. Intermediate models include major mechanisms that affect dispersal, in addition to the dispersal curve of a phenomenological model. Here we review and describe the mathematical and statistical framework for phenomenological dispersal modelling. In the mathematical development we describe modelling of dispersal in two dimensions from a point source, and in one dimension from a line or area source. In the statistical development we describe applicable observation distributions, and the procedures of model fitting, comparison, checking, and prediction. The procedures are also demonstrated using data from dispersal experiments. The data are hierarchically structured, and hence, we fit hierarchical models. The Bayesian modelling approach is applied, which allows us to show the uncertainty in the parameter estimates and in predictions. Finally, we show how to account for the effect of wind speed on the estimates of the dispersal parameters. This serves as an example of how to strengthen the coupling in the modelling between the phenomenon observed in an experiment and the underlying process – something that should be striven for in the statistical modelling of dispersal.     

A general framework for propagule dispersal in mangroves

Dispersal allows species to shift their distributions in response to changing climate conditions. As a result, dispersal is considered a key process contributing to a species' long‐term persistence. For many passive dispersers, fluid dynamics of wind and water fuel these movements and different species have developed remarkable adaptations for utilizing this energy to reach and colonize suitable habitats. The seafaring propagules (fruits and seeds) of mangroves represent an excellent example of such passive dispersal. Mangroves are halophytic woody plants that grow in the intertidal zones along tropical and subtropical shorelines and produce hydrochorous propagules with high dispersal potential. This results in exceptionally large coastal ranges across vast expanses of ocean and allows species to shift geographically and track the conditions to which they are adapted. This is particularly relevant given the challenges presented by rapid sea‐level rise, higher frequency and intensity of storms, and changes in regional precipitation and temperature regimes. However, despite its importance, the underlying drivers of mangrove dispersal have typically been studied in isolation, and a conceptual synthesis of mangrove oceanic dispersal across spatial scales is lacking. Here, we review current knowledge on mangrove propagule dispersal across the various stages of the dispersal process. Using a general framework, we outline the mechanisms and ecological processes that are known to modulate the spatial patterns of mangrove dispersal. We show that important dispersal factors remain understudied and that adequate empirical data on the determinants of dispersal are missing for most mangrove species. This review particularly aims to provide a baseline for developing future research agendas and field campaigns, filling current knowledge gaps and increasing our understanding of the processes that shape global mangrove distributions.     

A unifying framework for amyloid-mediated membrane damage: The lipid-chaperone hypothesis

Over the past thirty years, researchers have highlighted the role played by a class of proteins or polypeptides that forms pathogenic amyloid aggregates in vivo, including i) the amyloid Aβ peptide, which is known to form senile plaques in Alzheimer's disease; ii) α-synuclein, responsible for Lewy body formation in Parkinson's disease and iii) IAPP, which is the protein component of type 2 diabetes-associated islet amyloids. These proteins, known as intrinsically disordered proteins (IDPs), are present as highly dynamic conformational ensembles.IDPs can partially (mis) fold into (dys) functional conformations and accumulate as amyloid aggregates upon interaction with other cytosolic partners such as proteins or lipid membranes. In addition, an increasing number of reports link the toxicity of amyloid proteins to their harmful effects on membrane integrity. Still, the molecular mechanism underlying the amyloidogenic proteins transfer from the aqueous environment to the hydrocarbon core of the membrane is poorly understood.This review starts with a historical overview of the toxicity models of amyloidogenic proteins to contextualize the more recent lipid-chaperone hypothesis. Then, we report the early molecular-level events in the aggregation and ion-channel pore formation of Aβ, IAPP, and α-synuclein interacting with model membranes, emphasizing the complexity of these processes due to their different spatial-temporal resolutions. Next, we underline the need for a combined experimental and computational approach, focusing on the strengths and weaknesses of the most commonly used techniques. Finally, the last two chapters highlight the crucial role of lipid-protein complexes as molecular switches among ion-channel-like formation, detergent-like, and fibril formation mechanisms and their implication in fighting amyloidogenic diseases.     

A unifying framework for chaos and stochastic stability in discrete population models

 In this paper we propose a general framework for discrete time one-dimensional Markov population models which is based on two fundamental premises in population dynamics. We show that this framework incorporates both earlier population models, like the Ricker and Hassell models, and experimental observations concerning the structure of density dependence. The two fundamental premises of population dynamics are sufficient to guarantee that the model will exhibit chaotic behaviour for high values of the natural growth and the density-dependent feedback, and this observation is independent of the particular structure of the model. We also study these models when the environment of the population varies stochastically and address the question under what conditions we can find an invariant probability distribution for the population under consideration. The sufficient conditions for this stochastic stability that we derive are of some interest, since studying certain statistical characteristics of these stochastic population processes may only be possible if the process converges to such an invariant distribution. Received 15 May 1995; received in revised form 17 April 1996     

A unifying framework for seed sensitivity and its application to subset seeds

We propose a general approach to compute the seed sensitivity, that can be applied to different definitions of seeds. It treats separately three components of the seed sensitivity problem--a set of target alignments, an associated probability distribution, and a seed model--that are specified by distinct finite automata. The approach is then applied to a new concept of subset seeds for which we propose an efficient automaton construction. Experimental results confirm that sensitive subset seeds can be efficiently designed using our approach, and can then be used in similarity search producing better results than ordinary spaced seeds.     

Long-distance dispersal: a framework for hypothesis testing

Tests of hypotheses about the biogeographical consequences of long-distance dispersal have long eluded biologists, largely because of the rarity and presumed unpredictability of such events. Here, we examine data for terrestrial (including littoral) organisms in the Pacific to show that knowledge of dispersal by wind, birds and oceanic drift or rafting, coupled with information about the natural environment and biology of the organisms, can be used to generate broad biogeographic predictions. We then examine the predictions in the context of the origin, frequency of arrival and location of establishment of dispersed organisms, as well as subsequent patterns of endemism and diversification on remote islands. The predicted patterns are being increasingly supported by phylogenetic data for both terrestrial and littoral organisms.     

A unifying quantitative framework for exploring the multiple facets of microbial biodiversity across diverse scales

Recent developments of molecular tools have revolutionized our knowledge of microbial biodiversity by allowing detailed exploration of its different facets and generating unprecedented amount of data. One key issue with such large datasets is the development of diversity measures that cope with different data outputs and allow comparison of biodiversity across different scales. Diversity has indeed three components: local (α), regional (γ) and the overall difference between local communities (β). Current measures of microbial diversity, derived from several approaches, provide complementary but different views. They only capture the β component of diversity, compare communities in a pairwise way, consider all species as equivalent or lack a mathematically explicit relationship among the α, β and γ components. We propose a unified quantitative framework based on the Rao quadratic entropy, to obtain an additive decomposition of diversity (γ = α + β), so the three components can be compared, and that integrate the relationship (phylogenetic or functional) among Microbial Diversity Units that compose a microbial community. We show how this framework is adapted to all types of molecular data, and we highlight crucial issues in microbial ecology that would benefit from this framework and propose ready‐to‐use R‐functions to easily set up our approach.     

Diversity indices for ecological networks: a unifying framework using Hill numbers

Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α‐, γ‐ and β‐diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food‐web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time.     

Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses a set of peristimulus-time histograms computed from single-unit spike trains in response to polarity-alternating stimuli to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less confounded by distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss.     

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.     

The multiple decrement life table: a unifying framework for cause-of-death analysis in ecology

Summary The multiple decrement life table is used widely in the human actuarial literature and provides statistical expressions for mortality in three different forms: i) the life table from all causes-of-death combined; ii) the life table disaggregated into selected cause-of-death categories; and iii) the life table with particular causes and combinations of causes eliminated. The purpose of this paper is to introduce the multiple decrement life table to the ecological literature by applying the methods to published death-by-cause information on Rhagoletis pomonella. Interrelations between the current approach and conventional tools used in basic and applied ecology are discussed including the conventional life table, Key Factor Analysis and Abbott's Correction used in toxicological bioassay.     

A unifying concept for carcinogenic risk assessments

Carcinogens influence both the initiation of abnormal cells and the subsequent promotion of such cells into neoplasia. Certain other insults seem limited to the stimulation of cellular proliferation and of carcinogenic potentiation. Common examples include surgical, mechanical, chemical, and temperature wounding of tissue followed by healing. In addition, certain hyperplastic growth induced by some chemicals may also enhance tumorigenesis. We propose that the quantification of carcinogenic potentiation may derive from a common-index-quantity estimated according to enhanced cell proliferation resulting from cytotoxicity or toxic hyperplasia induced by a specific exposure. At this time, it is not possible to define, in a restrictive sense, the molecular events which are critical to potentiation but the processes of cell proliferation resulting from cytotoxicity/hyperplasia seem to serve as indices which contain the necessary (and perhaps several secondary) biological responses. The unique advantage is that cell-culture, animal, and human-level studies can be used to evaluate certain parameters of the mathematical model for an untested treatment protocol or chemical insult suspected to be a cofactor in tumorigenesis. The main thrust of this paper is to propose that tumorigenesis should be studied in terms of cellular-population kinetics in response to a biological challenge rather than according to chemical or energetic parameters of that challenge.This approach leads to mathematical equations which can serve as a unifying concept for carcinogenic risk assessments. Sample results, to illustrate the utility of this model, are given for polynuclear aromatic hydrocarbons, trace metals, ionizing radiations, CO, NO, SO₂, O₃, and NO₂. Treatment, here, is for acute exposure conditions, but because the model is mechanistic, other exposure protocols can be addressed by simply adjusting some of the mathematical parameters according to factors estimated from a relative potency comparison of in vitro and in vivo studies best suited to the particular application of interest.     

A unifying framework for assessing changes in life expectancy associated with changes in mortality: the case of violent deaths

For over forty years, demographers have worked intensely to develop methods that assess a gain in life expectancy from a reduction in mortality, either hypothetical or observed. This considerable body of research was motivated by assessing the gains in life expectancy when mortality declined in a particular manner and determining the contribution of a cause of death in observed changes in life expectancy over time. As yet, there has been no framework unifying this important demographic work. In this paper, we provide a unifying framework for assessing the change in life expectancy given a change in age- and cause-specific mortality. We consider both conceptualizations of mortality change—counterfactual assessment of a hypothetical change and a retrospective assessment of an observed change. We apply our methodology to violent deaths, the leading cause of death among young adults, and show that realistic targeted reductions could have important impacts on life expectancy.     

A model-based framework for assessing the vulnerability of low dispersal vertebrates to landscape fragmentation under environmental change

Environmental changes are driving rapid geographic shifts of suitable environmental conditions for species. These might survive by tracking those shifts, however successful responses will depend on the spatial distribution of suitable habitats (current and future) and on their connectivity. Most herptiles (i.e., amphibians and reptiles) have low dispersal abilities, and therefore herptiles are among the most vulnerable groups to environmental changes. Here we assessed the vulnerability of herptile species to future climate and land use changes in fragmented landscapes. We developed and tested a methodological approach combining the strengths of Species Distribution Models (SDMs) and of functional connectivity analysis. First, using SDMs we forecasted current and future distributions of potential suitable areas as well as range dynamics for four herptile species in Portugal. SDM forecasts for 2050 were obtained under two contrasting emission scenarios, translated into moderate (low-emissions scenario) or large (high-emissions scenario) changes in climate and land use conditions. Then, we calculated and analysed functional connectivity from areas projected to lose environmental suitability towards areas keeping suitable conditions. Landscape matrix resistance and barrier effects of the national motorway network were incorporated as the main sources of fragmentation. Potential suitable area was projected to decrease under future conditions for most test species, with the high-emissions scenario amplifying the losses or gains. Spatiotemporal patterns of connectivity between potentially suitable areas signalled the most important locations for maintaining linkages and migration corridors, as well as potential conflicts due to overlaps with the current motorway network. By integrating SDM projections with functional connectivity analysis, we were able to assess and map the vulnerability of distinct herptile species to isolation or extinction under environmental change scenarios. Our framework provides valuable information, with fairly low data requirements, for optimizing biodiversity management and mitigation efforts, aiming to reduce the complex and often synergistic negative impacts of multiple environmental change drivers. Implications for conservation planning and management are discussed from a global change adaptation perspective.