A decade since ”diversification of ruminants”: has our knowledge improved?

In his landmark 1989 paper, R.R. Hofmann classified ruminants into three categories based upon digestive anatomy and preferred forages, and proposed that divergence of feeding strategies among ruminants is a result of morphological evolution of the digestive tract. Because of the hypothetical nature of these views and the ingrained beliefs that they challenged, several papers were published that reported tests of Hofmann’s predictions. The consensus among these papers was that Hofmann’s predictions were inadequate. I describe the experimental evidence that has been put forth in opposition to the ruminant diversification hypothesis and contend that we have failed to adequately test Hofmann’s predictions. Received: 11 October 1999 / Accepted: 6 April 2000     

Mutualistic networks: moving closer to a predictive theory

Plant–animal mutualistic networks sustain terrestrial biodiversity and human food security. Global environmental changes threaten these networks, underscoring the urgency for developing a predictive theory on how networks respond to perturbations. Here, I synthesise theoretical advances towards predicting network structure, dynamics, interaction strengths and responses to perturbations. I find that mathematical models incorporating biological mechanisms of mutualistic interactions provide better predictions of network dynamics. Those mechanisms include trait matching, adaptive foraging, and the dynamic consumption and production of both resources and services provided by mutualisms. Models incorporating species traits better predict the potential structure of networks (fundamental niche), while theory based on the dynamics of species abundances, rewards, foraging preferences and reproductive services can predict the extremely dynamic realised structures of networks, and may successfully predict network responses to perturbations. From a theoretician's standpoint, model development must more realistically represent empirical data on interaction strengths, population dynamics and how these vary with perturbations from global change. From an empiricist's standpoint, theory needs to make specific predictions that can be tested by observation or experiments. Developing models using short‐term empirical data allows models to make longer term predictions of community dynamics. As more longer term data become available, rigorous tests of model predictions will improve.     

Placing empirical limits on metapopulation models for terrestrial plants

Both island-biogeographic (dynamic) and niche-based (static) metapopulation models make predictions about the distribution and abundance of species assemblages. We tested the utility of these models concerning such predictions for terrestrial vascular plants using data from 74 landscapes across the globe. We examined correlations between species frequency and local abundance and shapes of the species frequency distribution. No data set met all of the predictions of any single island-biogeographic metapopulation model. In contrast, all data sets met the predictions of the niche-based model. We conclude that in predicting the distribution of species assemblages of plants over scales greater than 10–1 km, niche-based models are robust while current metapopulation models are insufficient. We discuss limitations in the assumptions of the various models and the types of empirical observations that they will each have to deal with in further developments.     

When individual life history matters: conditions for juvenile-adult stage structure effects on population dynamics

Ecological theory about the dynamics of interacting populations is mainly based on unstructured models that account for species abundances only. In turn, these models constitute the basis for our understanding of the functioning of ecological communities and ecosystems and their responses to environmental change, natural disturbances and human impacts. Structured models that take into account differences between individuals in age, stage or size have been shown to sometimes make predictions that run counter to the predictions of unstructured analogues. It is however unclear which biological mechanisms that are accounted for in the structured models give rise to these contrasting predictions. Focusing on two particular rules-of-thumb that generally hold in unstructured consumer-resource models, one relating to the relationship between mortality and equilibrium density of the consumer and the other relating to the stability of the equilibrium, I investigate the necessary conditions under which accounting for juvenile-adult stage structure can lead to qualitatively different model predictions. In particular, juvenile-adult stage structure is shown to overturn the two rules-of-thumb in case the model also accounts for the energetic requirements for basic metabolic maintenance. Given the fundamental nature of both juvenile-adult stage structure as well as metabolic maintenance requirements, these results call into question the generality of the predictions derived from unstructured models.     

Systematic Artifacts in Support Vector Regression-Based Compound Potency Prediction Revealed by Statistical and Activity Landscape Analysis

Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and pharmaceutical research, SVR has become the probably most popular approach for modeling of non-linear structure-activity relationships (SARs) and predicting compound potency values. Herein, we have systematically generated and analyzed SVR prediction models for a variety of compound data sets with different SAR characteristics. Although these SVR models were accurate on the basis of global prediction statistics and not prone to overfitting, they were found to consistently mispredict highly potent compounds. Hence, in regions of local SAR discontinuity, SVR prediction models displayed clear limitations. Compared to observed activity landscapes of compound data sets, landscapes generated on the basis of SVR potency predictions were partly flattened and activity cliff information was lost. Taken together, these findings have implications for practical SVR applications. In particular, prospective SVR-based potency predictions should be considered with caution because artificially low predictions are very likely for highly potent candidate compounds, the most important prediction targets.     

Using a classic paper by Gottschalk and Mylle to teach the countercurrent model of urinary concentration

Most undergraduates lack the scientific background to read and appreciate much of the primary literature in physiology. Even when the underlying concepts are elegantly simple, the inherent complexity of contemporary papers often makes the work inaccessible to them. However, with a little help, they can be guided to an understanding of the creative thought processes that underlie the research and to appreciate its significance. This is especially true of many classic papers in physiology that often rely on easily comprehensible techniques. Moreover, the American Physiological Society (APS) has invited prominent scientists to select important papers in their fields and to write essays that both put the work into historical context and explain why it is scientifically important. The APS Legacy Project makes these classic papers freely available online. One such paper by Gottschalk and Mylle presents data from a series of micropuncture studies that confirm all of the predictions of the countercurrent exchange model of concentrated urine production (2). The included handout of questions for discovery learning and teaching points suggest ways to use the paper as an instructional resource.     

A new genomic evolutionary model for rearrangements, duplications, and losses that applies across eukaryotes and prokaryotes

Genomic rearrangements have been studied since the beginnings of modern genetics and models for such rearrangements have been the subject of many papers over the last 10 years. However, none of the extant models can predict the evolution of genomic organization into circular unichromosomal genomes (as in most prokaryotes) and linear multichromosomal genomes (as in most eukaryotes). Very few of these models support gene duplications and losses--yet these events may be more common in evolutionary history than rearrangements and themselves cause apparent rearrangements. We propose a new evolutionary model that integrates gene duplications and losses with genome rearrangements and that leads to genomes with either one (or a very few) circular chromosome or a collection of linear chromosomes. Our model is based on existing rearrangement models and inherits their linear-time algorithms for pairwise distance computation (for rearrangement only). Moreover, our model predictions fit observations about the evolution of gene family sizes and agree with the existing predictions about the growth in the number of chromosomes in eukaryotic genomes.     

Size control models of Saccharomyces cerevisiae cell proliferation.

By using time-lapse photomicroscopy, the individual cycle times and sizes at bud emergence were measured for a population of saccharomyces cerevisiae cells growing exponentially under balanced growth conditions in a specially constructed filming slide. There was extensive variability in both parameters for daughter and parent cells. The data on 162 pairs of siblings were analyzed for agreement with the predictions of the transition probability hypothesis and the critical-size hypothesis of yeast cell proliferation and also with a model incorporating both of these hypotheses in tandem. None of the models accounted for all of the experimental data, but two models did give good agreement to all of the data. The wobbly tandem model proposes that cells need to attain a critical size, which is very variable, enabling them to enter a start state from which they exit with first order kinetics. The sloppy size control model suggests that cells have an increasing probability per unit time of traversing start as they increase in size, reaching a high plateau value which is less than one. Both models predict that the kinetics of entry into the cell division sequence will strongly depend on variability in birth size and thus will be quite different for daughters and parents of the asymmetrically dividing yeast cells. Mechanisms underlying these models are discussed.     

Integrating demography,dispersal and interspecific interactions into bird distribution models

Species’ ranges are primarily limited by the physiological (abiotic) tolerance of the species, described by their fundamental niche. Additionally, demographic processes, dispersal, and interspecific interactions with other species are shaping species distributions, resulting in the realised niche. Understanding the complex interplay between these drivers is vital for making robust biodiversity predictions to novel environments. Correlative species distribution models have been widely used to predict biodiversity response but also remain criticised, as they are not able to properly disentangle the abiotic and biotic drivers shaping species’ niches. Recent developments have thus focussed on 1) integrating demography and dispersal into species distribution models, and on 2) integrating interspecific interactions. Here, I review recent demographic and multi‐species modelling approaches and discuss critical aspects of these models that remain underexplored in general and in respect to birds, for example, the complex life histories of birds and other animals as well as the scale dependence of interspecific interactions. I conclude by formulating modelling guidelines for integrating the abiotic and biotic processes that limit species’ ranges, which will help to disentangle the complex roles of demography, dispersal and interspecific interactions in shaping species niches. Throughout, I pinpoint complexities of avian life cycles that are critical for consideration in the models and identify data requirements for operationalizing the different modelling steps.     

Bovine tuberculosis in brushtail possums: models, dogma and data

Three different models of bovine tuberculosis (Tb) in brushtail possums were evaluated against their stated purpose, and testable assumptions and predictions evaluated against available data where possible. Not surprisingly, two of the models may be falsified based on currently available data with respect to either important model assumptions or predictions, and the third may suffer from being right for the wrong reason. This does not mean that these models are not useful. To the contrary, I argue that all models, especially those published in the scientific literature have largely addressed their stated purpose, and have contributed to our understanding of and ability to manage bovine tuberculosis infection in brushtail possum populations. No model, however, satisfactorily explains the pronounced spatial clustering of possum Tb, and the models critiqued have provided little strong inference as to the routes of transmission of Tb among possums. This situation is not helped by the scarcity of datasets on Tb in uncontrolled possum populations that are readily available to confront competing possum/Tb models with. As time passes, there is a very real risk that these data sets will be lost. This is of particular concern, as the expansion in the area of New Zealand under active possum management means the future opportunity to collect further data on Tb in uncontrolled possum populations is severely limited.     

Bayesian data analysis in population ecology: motivations,methods, and benefits

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.     

Seascape genetics: a coupled oceanographic-genetic model predicts population structure of Caribbean corals

Population genetics is a powerful tool for measuring important larval connections between marine populations [1-4]. Similarly, oceanographic models based on environmental data can simulate particle movements in ocean currents and make quantitative estimates of larval connections between populations possible [5-9]. However, these two powerful approaches have remained disconnected because no general models currently provide a means of directly comparing dispersal predictions with empirical genetic data (except, see [10]). In addition, previous genetic models have considered relatively simple dispersal scenarios that are often unrealistic for marine larvae [11-15], and recent landscape genetic models have yet to be applied in a marine context [16-20]. We have developed a genetic model that uses connectivity estimates from oceanographic models to predict genetic patterns resulting from larval dispersal in a Caribbean coral. We then compare the predictions to empirical data for threatened staghorn corals. Our coupled oceanographic-genetic model predicts many of the patterns observed in this and other empirical datasets; such patterns include the isolation of the Bahamas and an east-west divergence near Puerto Rico [3, 21-23]. This new approach provides both a valuable tool for predicting genetic structure in marine populations and a means of explicitly testing these predictions with empirical data.     

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.     

Cellular-based modeling of oscillatory dynamics in brain networks

Oscillatory, population activities have long been known to occur in our brains during different behavioral states. We know that many different cell types exist and that they contribute in distinct ways to the generation of these activities. I review recent papers that involve cellular-based models of brain networks, most of which include theta, gamma and sharp wave-ripple activities. To help organize the modeling work, I present it from a perspective of three different types of cellular-based modeling: 'Generic', 'Biophysical' and 'Linking'. Cellular-based modeling is taken to encompass the four features of experiment, model development, theory/analyses, and model usage/computation. The three modeling types are shown to include these features and interactions in different ways.     

Implementation of Contraction to Electrophysiological Ventricular Myocyte Models,and Their Quantitative Characterization via Post-Extrasystolic Potentiation

Heart failure (HF) affects over 5 million Americans and is characterized by impairment of cellular cardiac contractile function resulting in reduced ejection fraction in patients. Electrical stimulation such as cardiac resynchronization therapy (CRT) and cardiac contractility modulation (CCM) have shown some success in treating patients with HF. Computer simulations have the potential to help improve such therapy (e.g. suggest optimal lead placement) as well as provide insight into the underlying mechanisms which could be beneficial. However, these myocyte models require a quantitatively accurate excitation-contraction coupling such that the electrical and contraction predictions are correct. While currently there are close to a hundred models describing the detailed electrophysiology of cardiac cells, the majority of cell models do not include the equations to reproduce contractile force or they have been added ad hoc. Here we present a systematic methodology to couple first generation contraction models into electrophysiological models via intracellular calcium and then compare the resulting model predictions to experimental data. This is done by using a post-extrasystolic pacing protocol, which captures essential dynamics of contractile forces. We found that modeling the dynamic intracellular calcium buffers is necessary in order to reproduce the experimental data. Furthermore, we demonstrate that in models the mechanism of the post-extrasystolic potentiation is highly dependent on the calcium released from the Sarcoplasmic Reticulum. Overall this study provides new insights into both specific and general determinants of cellular contractile force and provides a framework for incorporating contraction into electrophysiological models, both of which will be necessary to develop reliable simulations to optimize electrical therapies for HF.     

Models of the population genetics of transposable elements

Although transposable elements (TEs) have been found in all organisms in which they have been looked for, the ways in which they invade genomes and populations are still a matter of debate. By extending the classical models of population genetics, several approaches have been developed to account for the dynamics of TEs, especially in Drosophila melanogaster . While the formalism of these models is based on simplifications, they enable us to understand better how TEs invade genomes, as a result of multiple evolutionary forces including duplication, deletion, self-regulation, natural selection and genetic drift. The aim of this paper is to review the assumptions and the predictions of these different models by highlighting the importance of the specific characteristics of both the TEs and the hosts, and the host/TE relationships. Then, perspectives in this domain will be discussed.     

Fungal biodiversity: what do we know? What can we predict?

Although fungi are among the most important organisms in the world, only limited and incomplete information is currently available for most species and current estimates of species numbers for fungi differ significantly. This lack of basic information on taxonomic diversity has significant implications for many aspects of evolutionary biology. While the figure of 1.5 million estimated fungal species is commonly used, critics have questioned the validity of this estimate. Data on biogeographic distributions, levels of endemism, and host specificity must be taken into account when developing estimates of global fungal diversity. This paper introduces a set of papers that attempt to develop a rigorous, minimum estimate of global fungal diversity based on a critical assessment of current species lists and informed predictions of missing data and levels of endemism. As such, these papers represent both a meta-analysis of current data and a gap assessment to indicate where future research efforts should be concentrated.     

Nesting habitat preference of the African White-backed Vulture Gyps africanus and the effects of anthropogenic disturbance

The African White-backed Vulture Gyps africanus is widely distributed across sub-Saharan Africa but populations are in decline. Loss of suitable habitat for foraging and breeding are among the most important causes, and future conservation will require identification of suitable remaining habitat and the threats to it and to the vultures in it. Like many large raptors, African White-backed Vultures have a long breeding cycle and thus spend much of each year near their nest site, but ecological correlates of nest sites have not been quantified for any African vulture species. We use nest-site data for African White-backed Vultures collected during aerial and ground surveys and habitat data derived from a GIS to develop statistical models that estimate the probability of nest presence in relation to habitat characteristics, and test these models against an independent dataset. The models predicted that both direct and indirect disturbance by humans limit the potential distribution. Suitable habitat needs to be identified and receive adequate protection from poaching. Poaching of vultures is thought to be mainly for use in traditional medicine and does not target any particular species, so all vulture species can be considered equally at risk. We predict the likelihood of individuals nesting in currently unprotected areas should they become protected. These predictions show that readily available GIS data combined with relatively simple statistical modelling can provide meaningful large-scale predictions of habitat availability.