Mapping the complexity of ecological models

We propose to define the complexity of an ecological model as the statistical complexity of the output it produces. This allows for a direct comparison between data and model complexity. Working with univariate time series, we show that this measure ‘blindly’ discriminates among the different dynamical behaviours a model can exhibit. We then search a model parameter space in order to segment it into areas of different dynamical behaviour and calculate the maximum complexity a model can generate. Given a time series, and the problem of choosing among a number of ecological models to study it, we suggest that models whose maximum complexity is lower than the time series complexity should be disregarded because they are unable to reconstruct some of the structures contained in the data. Similar reasoning could be used to disregard models’ subdomains as well as areas of unnecessary high complexity. We suggest that model complexity so defined better captures the difficulty faced by a user in managing and understanding the behaviour of an ecological model than measures based on a model ‘size’.     

Optimal synthesis of chromatographic trains for downstream protein processing

Downstream bioprocessing and especially chromatographic steps, commonly used for the purification of multicomponent systems, are significant cost drivers in the production of therapeutic proteins. There has been an increased interest in the development of systematic methods for the design of such processes, and the appropriate selection of a series of chromatographic steps is still a major challenge to be addressed. Several models have been developed previously but have assumed that 100% recovery of the desired product is obtained at each chromatographic step. In this work, a mathematical framework is proposed, based on mixed integer optimisation techniques, that removes this assumption and allows full flexibility on the position of retention time cut-points, between which the desired product fraction is collected. The proposed model is demonstrated on three example protein mixtures, each containing up to 13 contaminants and selecting from a set of up to 21 candidate steps. The proposed model results in a reduction of one to three chromatographic steps over solutions that no losses are allowed.     

Complexity is costly: a meta‐analysis of parametric and non‐parametric methods for short‐term population forecasting

Short‐term forecasts based on time series of counts or survey data are widely used in population biology to provide advice concerning the management, harvest and conservation of natural populations. A common approach to produce these forecasts uses time‐series models, of different types, fit to time series of counts. Similar time‐series models are used in many other disciplines, however relative to the data available in these other disciplines, population data are often unusually short and noisy and models that perform well for data from other disciplines may not be appropriate for population data. In order to study the performance of time‐series forecasting models for natural animal population data, we assembled 2379 time series of vertebrate population indices from actual surveys. Our data were comprised of three vastly different types: highly variable (marine fish productivity), strongly cyclic (adult salmon counts), and small variance but long‐memory (bird and mammal counts). We tested the predictive performance of 49 different forecasting models grouped into three broad classes: autoregressive time‐series models, non‐linear regression‐type models and non‐parametric time‐series models. Low‐dimensional parametric autoregressive models gave the most accurate forecasts across a wide range of taxa; the most accurate model was one that simply treated the most recent observation as the forecast. More complex parametric and non‐parametric models performed worse, except when applied to highly cyclic species. Across taxa, certain life history characteristics were correlated with lower forecast error; specifically, we found that better forecasts were correlated with attributes of slow growing species: large maximum age and size for fishes and high trophic level for birds. Synthesis Evaluating the data support for multiple plausible models has been an integral focus of many ecological analyses. However, the most commonly used tools to quantify support have weighted models’ hindcasting and forecasting abilities. For many applications, predicting the past may be of little interest. Concentrating only on the future predictive performance of time series models, we performed a forecasting competition among many different kinds of statistical models, applying each to many different kinds of vertebrate time series of population abundance. Low‐dimensional (simple) models performed well overall, but more complex models did slightly better when applied to time series of cyclic species (e.g. salmon).     

Moment Approximation of Infection Dynamics in a Population of Moving Hosts

The modelling of contact processes between hosts is of key importance in epidemiology. Current studies have mainly focused on networks with stationary structures, although we know these structures to be dynamic with continuous appearance and disappearance of links over time. In the case of moving individuals, the contact network cannot be established. Individual-based models (IBMs) can simulate the individual behaviours involved in the contact process. However, with very large populations, they can be hard to simulate and study due to the computational costs. We use the moment approximation (MA) method to approximate a stochastic IBM with an aggregated deterministic model. We illustrate the method with an application in animal epidemiology: the spread of the highly pathogenic virus H5N1 of avian influenza in a poultry flock. The MA method is explained in a didactic way so that it can be reused and extended. We compare the simulation results of three models: 1. an IBM, 2. a MA, and 3. a mean-field (MF). The results show a close agreement between the MA model and the IBM. They highlight the importance for the models to capture the displacement behaviours and the contact processes in the study of disease spread. We also illustrate an original way of using different models of the same system to learn more about the system itself, and about the representation we build of it.     

Liver porphyrinogen carboxylase in hexachlorobenzene porphyric rats. Studies with intermediate porphyrinogens of series III and with uroporphyrinogen I

The present work studies the action of hexachlorobenzene (HCB) on the decarboxylation of uroporphyrinogen (Urogen) I and III and also on the decarboxylation of intermediate porphyrinogens of series III under different conditions using liver of normal and porphyric rats as enzyme source. The same enzyme is involved in the Urogen decarboxylation of both isomeric series I and III and catalyses the four steps in both cases. HCB affects all of them. HCB blocks the four steps of Urogen III decarboxylation to the same degree, as a function of intoxication time. HCB leads, in general, to an increase in the efficiency (Km/Vmax) of the porphyric system. These data can be interpreted as a reaction of the organism to overcome the enzymatic blockade.     

Reasons to be fussy about cultural evolution

This discussion paper responds to two recent articles in Biology and Philosophy that raise similar objections to cultural attraction theory, a research trend in cultural evolution putting special emphasis on the fact that human minds create and transform their culture. Both papers are sympathetic to this idea, yet both also regret a lack of consilience with Boyd, Richerson and Henrich’s models of cultural evolution. I explain why cultural attraction theorists propose a different view on three points of concern for our critics. I start by detailing the claim that cultural transmission relies not chiefly on imitation or teaching, but on cognitive mechanisms like argumentation, ostensive communication, or selective trust, whose evolved or habitual function may not be the faithful reproduction of ideas or behaviours. Second, I explain why the distinction between context biases and content biases might not always be the best way to capture the interactions between culture and cognition. Lastly, I show that cultural attraction models cannot be reduced to a model of guided variation, which posits a clear separation between individual and social learning processes. With cultural attraction, the same cognitive mechanisms underlie both innovation and the preservation of traditions.     

A general framework for modeling population abundance data

Time-series data resulting from surveying wild animals are often described using state-space population dynamics models, in particular with Gompertz, Beverton-Holt, or Moran-Ricker latent processes. We show how hidden Markov model methodology provides a flexible framework for fitting a wide range of models to such data. This general approach makes it possible to model abundance on the natural or log scale, include multiple observations at each sampling occasion and compare alternative models using information criteria. It also easily accommodates unequal sampling time intervals, should that possibility occur, and allows testing for density dependence using the bootstrap. The paper is illustrated by replicated time series of red kangaroo abundances, and a univariate time series of ibex counts which are an order of magnitude larger. In the analyses carried out, we fit different latent process and observation models using the hidden Markov framework. Results are robust with regard to the necessary discretization of the state variable. We find no effective difference between the three latent models of the paper in terms of maximized likelihood value for the two applications presented, and also others analyzed. Simulations suggest that ecological time series are not sufficiently informative to distinguish between alternative latent processes for modeling population survey data when data do not indicate strong density dependence.     

kakusan: a computer program to automate the selection of a nucleotide substitution model and the configuration of a mixed model on multilocus data

The application of different substitution models to each gene (a.k.a. mixed model) should be considered in model‐based phylogenetic analysis of multigene sequences. However, a single molecular evolution model is still usually applied. There are no computer programs able to conduct model selection for multiple loci at the same time, though several recently developed types of software for phylogenetic inference can handle mixed model. Here, I have developed computer software named ‘kakusan’ that enables us to solve the above problems. Major running steps are briefly described, and an analysis of results with kakusan is compared to that obtained with other program.     

Testing eco-evolutionary predictions using fossil data: Phyletic evolution following ecological opportunity*

Fossil sequences provide observations of phenotypes within a lineage over time and represent essential data for increasing our understanding of phyletic evolution beyond microevolutionary timescales. I investigate if fossil time series of the diatom Stephanodiscus niagarae/yellowstonensis follow evolutionary dynamics compatible with hypotheses for how the adaptive landscape changes when a population enters a new environment. The lineage—which has a remarkably detailed stratigraphic record—invaded Yellowstone Lake immediately after recession of ice from the basin 14,000 years ago. Several phyletic models portraying different types of evolutionary dynamics—both compatible and not compatible with changes in the adaptive landscape following ecological opportunity—were fitted to the fossil times-series of S. niagarae/yellowstonensis. Different models best describe the three analyzed traits. Two of the models (a new model of decelerated evolution and an Ornstein–Uhlenbeck model) capture trait dynamics compatible with an event of ecological opportunity, whereas the third model (random walk) does not. Entering a new environment may accordingly affect trait dynamics for thousands of years, but the effects can vary across phenotypes. However, tests of model adequacy reveal shortcomings in all three models explaining the trait dynamics, suggesting model development is needed to more fully understand the phyletic evolution in S. niagarae/yellowstonensis.     

Prototype of simulation models for epizootics in domestic animals

Based on the Reed-Frost model (Model I), the authors conducted computer simulation of an epizootic model (Model II) constructed on the assumption that any infected animal in a group, after a given time-period of infectivity, would be removed from the group at the beginning of the next time-period. Models I and II were simulated 100 times for each of the different conditions, viz. the initial size of group, 100 and 1,000, the five steps of contact rate or contact size, and the five more steps of contact rate for the group of 1,000 animals in Model I. From the results obtained, it is believed that as a constant parameter, contact size may be preferably used instead of contact rate in these models. Model II mostly gave higher morbidities than Model I, and earlier termination of epizootics, except the simulation with the smallest contact size. This fact may be due to the effect of herd immunity involved only in Model I. The long duration of epizootic was demonstrated in two of the 100 simulations of Model II with 1,000 individuals and contact size 1. This is characteristic of probabilistic models which are really instructive to studying the flow of epizootic.     

Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus)

Behavioural syndromes are correlations between behaviours in different functional contexts. Behavioural syndromes are attracting the attention of evolutionary biologists because they mean that different behaviours might not be free to evolve independently of one another. In a landmark study, Huntingford (1976) showed that individual stickleback which were bold toward predators were also aggressive toward conspecifics and active in an unfamiliar environment. Here, I revisited the activity-aggression-boldness syndrome in stickleback and tested the hypothesis that correlations between behaviours might act as evolutionary constraints. I measured a suite of behaviours on wild-caught individuals and their offspring from two different populations and calculated heritabilities and genetic correlations between the different behaviours. I found that these behaviours were phenotypically and genetically correlated in one population but not another. On average, boldness and aggression were negatively related to each other across the populations. These results suggest that behavioural syndromes don't always act as evolutionary constraints.     

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.     

Comparing models for early warning systems of neglected tropical diseases

Background

Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.

Methodology/Principal Findings

In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R ² for forecasts of “out-of-fit” data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.

Conclusions/Significance

This study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of “out-of-fit” data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.     

Effects of human disturbance on movement,foraging and bed selection in red deer<Emphasis Type="Italic">Cervus elaphus xanthopygus</Emphasis> from the Wandashan Mountains,northeastern China

We characterized 716 sites (including 22 beds, 60 foraging and 604 movement sites) used by red deerCervus elaphus xanthopygus Milne-Edwards, 1867 in the Wandashan Mountains, northeastern China in the winter period. We used covariates for vegetation, topography, disturbances by other ungulates, and disturbance by humans to develop movement, forage and bed site resource-selection models. We used an information-theoretic approach to select the top 5 models for movement, forage and bed site occurrences respectively. The three most parsimonious autologistic models were good predictors of movement, forage and bed occurrence of this species. Vegetation covariates were important components of all models. We recorded avoidance responses by the behaviours of species to each disturbance type (ie, villages, forest roads and abandoned roads); bed site occurrences were most sensitive to all human disturbances for their lowest odds encountered. Across all parsimonious models, villages have the largest negative effect on movement, foraging and bed occurrences. Movement and bedding behaviours were also affected by other ungulate disturbances. Altitude had slightly negative effect on movement and foraging behaviours. Ridges, topographic aspect and shrub stands were all correlated with bed-site selection. Although different behaviours may be associated with different microhabitat features, behaviours of red deer responded similarly to the same human disturbance on a broad scale. Based on the observations above, we believe that red deer are avoiding human-altered habitat for movement, bedding and foraging because of disturbances during the critical late winter period. Furthermore, various behavioural resource selection models and corresponding graphs of important habitat disturbances can be used to guide and evaluate future development proposals.     

Polymorphic mimicry, microhabitat use, and sex-specific behaviour

In order to assess the adaptive importance of microhabitat segregation for the maintenance of mimetic diversity, I explore how flight height varies between the sympatric forms of the polymorphic butterfly Heliconius numata and their respective models in the genus Melinaea. There is no evidence for vertical stratification of mimicry rings in these tiger-patterned butterflies, but males of H. numata tend to fly significantly higher than females and the Melinaea models. This difference in microhabitat preference likely results from females searching for host plants whereas males are patrolling for mates. I then present an extension of Muller's mimicry model for the case of partial behavioural or spatial segregation of sexes. The analysis suggests that sex-specific behaviours can make mimicry more beneficial, simply by reducing the effective population size participating in mimicry. The interaction between mimicry and sex-specific behaviours may therefore facilitate the evolution of polymorphism via enhanced, fine-scale local adaptation.     

From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.     

Proteolytic activities of human fibroblast collagenase: hydrolysis of a broad range of substrates at a single active site

The action of human fibroblast collagenase (HFC) on six substrates of markedly different size, sequence, and conformation, including rat type I collagen, rat alpha 1(I) gelatin, beta-casein, and the three synthetic oligopeptides Gly-Pro-Gln-Gly-Ile-Ala-Gly-Gln, Asp-Val-Ala-Gln-Phe-Val-Leu-Thr-Pro-Gly, and Pro-Val-Gln-Pro-Ile-Gly-Pro-Gln, has been examined. The first peptide is a model for the collagenase cleavage site in the alpha 1(I) chain of type I collagen, while the latter two peptides are models for the autolytic activation and degradation sites in pro-HFC, respectively. The goal of these studies was to assess whether HFC hydrolyzes all of these disparate substrates at the same active site. Individual kinetic parameters for the hydrolysis of all six substrates have been determined. Gel zymography experiments using collagen, gelatin, and casein as substrates show that all three activities are associated solely with HFC rather than impurities. Recombinant HFC expressed in Escherichia coli also exhibits caseinase activity, reinforcing the view that this activity is not due to a contaminating protease from fibroblasts. The ratios of these activities agree within experimental error for several independent HFC preparations and do not change when two additional affinity purification steps are employed. The inhibition of the hydrolysis of these substrates by both 1,10-phenanthroline and Boc-Pro-Leu-Gly-NHOH is identical within experimental error. A series of assays carried out in the presence of pairs of these substrates clearly shows that they compete for the same active site.(ABSTRACT TRUNCATED AT 250 WORDS)     

Shoals and schools: back to the heuristic definitions and quantitative references

The terms ??shoal??, ??swarm?? and ??school?? are very frequently used in research on collective behaviours in animals. Pitcher??s definitions are accepted as the authority in the field but are based on a conceptual criterion of sociability. Without call into question the basis of these definitions, they do not provide tools to determine these behaviours quantitatively. To compare studies between populations, species, taxa or different experimental treatments, and between different authors, quantitative references are necessary. Quantitative measurements of collective behaviours can also test and validate the predictive capacity of computer models by comparing real data from nature so that different models can be compared. The first part of this paper succinctly reviews the definitions and meanings of these behaviours, with particular attention paid to quantitative aspects. This review underlines a series of conceptual confusions concerning these behavioural terms observed in the scientific literature and oral scientific communications. The second part reviews the quantitative parameters developed by biologists studying collective fish behaviours, mainly fish shoals, and by theoretical biologists and physicists studying computer modelling of collective behaviours. The parameters reviewed herein make no attempt to explain the mechanisms and causes that create a shoal, a swarm or a school, but rather try to describe these collective behaviours, and to connect local and global properties with individual and collective behaviours. Recent development over the last decade in technology, data processing capacity, cameras, and video tracking tools have provided the opportunity to obtain quantitative measures of collective dynamic behaviours in animals both rapidly and precisely.     

Virtual models of the HLA class I antigen processing pathway

Antigen recognition by cytotoxic CD8 T cells is dependent upon a number of critical steps in MHC class I antigen processing including proteosomal cleavage, TAP transport into the endoplasmic reticulum, and MHC class I binding. Based on extensive experimental data relating to each of these steps there is now the capacity to model individual antigen processing steps with a high degree of accuracy. This paper demonstrates the potential to bring together models of individual antigen processing steps, for example proteosome cleavage, TAP transport, and MHC binding, to build highly informative models of functional pathways. In particular, we demonstrate how an artificial neural network model of TAP transport was used to mine a HLA-binding database so as to identify HLA-binding peptides transported by TAP. This integrated model of antigen processing provided the unique insight that HLA class I alleles apparently constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3, and -A24) and those that are TAP-inefficient (HLA-A2, -B7, and -B8). Hence, using this integrated model we were able to generate novel hypotheses regarding antigen processing, and these hypotheses are now capable of being tested experimentally. This model confirms the feasibility of constructing a virtual immune system, whereby each additional step in antigen processing is incorporated into a single modular model. Accurate models of antigen processing have implications for the study of basic immunology as well as for the design of peptide-based vaccines and other immunotherapies.