Two branches,ecological and genetic,in studying the species population structure: History,problems, and solutions

This paper presents a brief history of two different methods for studying the species population structure. The first method employs ecological markers that characterize population-specific environmental conditions, as well as biological features of populations. The second one involves genetic markers: DNA and RNA fragments, allozymes, etc. The problem of combining these two methods is discussed. A two-step approach is suggested for studying the species population structure using both the ecological and genetic markers. Firstly, the studied part of the species range is subdivided into so-called ecogeographic units (EGUs) according to environmental gradients, life strategies, and other characteristics that presumably associate with adaptation gradients and interpopulation gene flows. Secondly, the EGUs are tested genetically by using the data on multiple population samples that represent population segments within each of the ecogeographic units. The notion of representative samples with respect to the population structure, hierarchy of EGUs–populations, strategies of population management, and selection of the management units for optimizing exploitation, reproduction, and conservation of species fragments are discussed on the basis of this approach.     

Formulating variable carrying capacity by exploring a resource dynamics-based feedback mechanism underlying the population growth models

Most of the population growth models comprise the concept of carrying capacity presume that a stable population would have a saturation level characteristic. This indicates that the population growth models have a common implicit feature of resource-limited growth, which contributes at a later stage of population growth by forming a numerical upper bound on the population size. However, a general underlying resource dynamics of the models has not been previously explored, which is the focus of present study. In this paper, we found that there exists a conservation of energy relationship comprising the terms of available resource and population density, jointly interpreted here as total available vital energy in a confined environment. We showed that this relationship determines a density-dependent functional form of relative population growth rate and consequently the parametric equations are in the form depending upon the population density, resource concentration, and time. Thus, the derived form of relative population growth rate is essentially a feedback type, i.e., updating parametric values for the corresponding population density. This resource dynamics-based feedback approach has been implemented for formulating variable carrying capacity in a confined environment. Particularly, at a constant resource replenishment rate, a density-dependent population growth equation similar to the classic logistic equation is derived, while one of the regulating factors of the underlying resource dynamics is that the resource consumption rate is directly proportional to the resource concentration. Likewise two other population growth equations similar to two known popular growth equations are derived based on this resource dynamics-based feedback approach. Using microcosm-derived data of fungus T. virens, we fitted one derived population growth model against the datasets, and concluded that this approach is practically implementable for studying a single population growth regulation in a confined environment.     

Remarks on branching-extinction evolutionary cycles

We show in this paper that the evolution of cannibalistic consumer populations can be a never ending story involving alternating levels of polymorphism. More precisely, we show that a monomorphic population can evolve toward high levels of cannibalism until it reaches a so-called branching point, where the population splits into two sub-populations characterized by different, but initially very close, cannibalistic traits. Then, the two traits coevolve until the more cannibalistic sub-population undergoes evolutionary extinction. Finally, the remaining population evolves back to the branching point, thus closing an evolutionary cycle. The model on which the study is based is purely deterministic and derived through the adaptive dynamics approach. Evolutionary dynamics are investigated through numerical bifurcation analysis, applied both to the ecological (resident-mutant) model and to the evolutionary model. The general conclusion emerging from this study is that branching-extinction evolutionary cycles can be present in wide ranges of environmental and demographic parameters, so that their detection is of crucial importance when studying evolutionary dynamics.     

Mathematical modeling of population dynamics of unstable plasmid-containing bacteria during continuous cultivation in a chemostat

A structural approach to studying the regularities of the population dynamics of unstable recombinant bacterial strains in a chemostat was elaborated. The approach is based on the mathematical modeling of cell distribution in a population with different numbers of plasmid copies. The effect of decreased selective preference of plasmidless variants of the recombinant strain in the chemostat, which is related to a decrease in the number of plasmid copies in cells upon long-term incubation was analyzed. It is shown that the time of half-elimination of plasmids from the bacterial population in the steady state in the chemostat T1/2 does not depend on the maximum number of plasmid copies in cells N but is determined only by the mean time of generation g and the probability of the loss of one plasmid copy tau. The dependence of the preference of bacterial plasmidless variants on the efficiency of expression of genes cloned into plasmids in chemostat was analyzed using the recombinant strain E. coli Z905, whose plasmids pPHL-7 contain cloned genes for the luminescence system of marine luminescing bacteria Photobacterium leiognathi.     

Quantifying population structure using the F-model

We review a model-based approach to estimate local population F(ST) 's that is based on the multinomial-Dirichlet distribution, the so-called F-model. As opposed to the standard method of estimating a single F(ST) value, this approach takes into account the fact that in most if not all realistic situations, local populations differ in their effective sizes and migration rates. Therefore, the use of this approach can help better describe the genetic structure of populations. Despite this obvious advantage, this method has remained largely underutilized by molecular ecologists. Thus, the objective of this review is to foster its use for studying the genetic structure of metapopulations. We present the derivation of the Bayesian formulation for the estimation of population-specific F(ST) 's based on the multinomial-Dirichlet distribution. We describe several recent applications of the F-model and present the results of a small simulation study that explains how the F-model can help better describe the genetic structure of populations.     

Food-web studies in shallow eutrophic lakes by the Netherlands Institute of Ecology: Main results,knowledge gaps and new perspectives

For more than 20 years scientists of the ‘Food-chain studies’ Group of the former Limnological Institute have been studying interactions within the pelagic food web. Purpose of research was to explain the structure and dynamics of the zooplankton and fish communities in lakes and reservoirs in relation to biotic and abiotic environmental factors. A so-called multi-species approach was used, in which all common and abundant species within a specific ecosystem were studied on the individual and population level with the same degree of detail. The recent results and the scientific approach used are evaluated and the main gaps in knowledge about food-web dynamics in shallow eutrophic lakes are identified and discussed. It is concluded that instead of the purely functional approach used so far, future studies should also include evolutionary aspects which determine the success of an organism in a given environment and that more attention should be paid to central questions in ‘community ecology’. This paper is based on a lecture given by the first author for the Netherlands Society of Aquatic Ecology on May 12th, 1992, in Amsterdam, The Netherlands.     

Caging and Uncaging Genetics

It is important for biology to understand if observations made in highly reductionist laboratory settings generalise to harsh and noisy natural environments in which genetic variation is sorted to produce adaptation. But what do we learn by studying, in the laboratory, a genetically diverse population that mirrors the wild? What is the best design for studying genetic variation? When should we consider it at all? The right experimental approach depends on what you want to know.     

Population structure of species: Eco-geographic units and genetic differentiation between populations

A two-step approach, based on a combined use of environmental, geographic, and genetic data, is suggested for studying population structures of species. First, populations are grouped into eco-geographic units (EGUs) according to the environmental gradients in the studied part of the species range, the types of life strategies, and other non-genetic characteristics that are presumably associated with adaptation and interpopulation gene flows. Second, the selected EGUs are tested for their congruence with genetic data by comparing the genetic differentiation between populations within EGUs to that between populations of different EGUs. Some of the issues discussed are as follows: the relationship of the EGU concept with the concepts of biogeocenosis and evolutionarily significant units (ESUs); designing EGUs in practice; the level of EGUs in a hierarchical population structure; and the weights of genetic and phenotypic markers in estimating population differentiation. The population structure of a salmonid fish, the Sakhalin taimen, in terms of eco-geographic units is considered as an example.     

Biochemical systems theory: increasing predictive power by using second-order derivatives measurements

Models based on the power-law formalism provide a useful tool for analyzing metabolic systems. Within this methodology, the S-system variant furnishes the best strategy. In this paper we explore an extension of this formalism by considering second-order derivative terms of the Taylor series which the power-law is based upon. Results show that the S-system equations which include second-order Taylor coefficients give better accuracy in predicting the response of the system to a perturbation. Hence, models based on this new approach could provide a useful tool for quantitative purposes if one is able to measure the required derivatives experimentally. In particular we show the utility of this approach when it comes to discriminating between two mechanisms that are equivalent in the S-system a representation based on first-order coefficients. However, the loss of analytical tractability is a serious disadvantage for using this approach as a general tool for studying metabolic systems.     

A Vaccination Model for a Multi-City System

A modelling approach is used for studying the effects of population vaccination on the epidemic dynamics of a set of n cities interconnected by a complex transportation network. The model is based on a sophisticated mover-stayer formulation of inter-city population migration, upon which is included the classical SIS dynamics of disease transmission which operates within each city. Our analysis studies the stability properties of the Disease-Free Equilibrium (DFE) of the full n-city system in terms of the reproductive number R (0). Should vaccination reduce R (0) below unity, the disease will be eradicated in all n-cities. We determine the precise conditions for which this occurs, and show that disease eradication by vaccination depend on the transportation structure of the migration network in a very direct manner. Several concrete examples are presented and discussed, and some counter-intuitive results found.     

Using dynamic vegetation models to simulate plant range shifts

Dynamic vegetation models (DVMs) follow a process‐based approach to simulate plant population demography, and have been used to address questions about disturbances, plant succession, community composition, and provisioning of ecosystem services under climate change scenarios. Despite their potential, they have seldom been used for studying species range dynamics explicitly. In this perspective paper, we make the case that DVMs should be used to this end and can improve our understanding of the factors that influence species range expansions and contractions. We review the benefits of using process‐based, dynamic models, emphasizing how DVMs can be applied specifically to questions about species range dynamics. Subsequently, we provide a critical evaluation of some of the limitations and trade‐offs associated with DVMs, and we use those to guide our discussions about future model development. This includes a discussion on which processes are lacking, specifically a mechanistic representation of dispersal, inclusion of the seedling stage, trait variability, and a dynamic representation of reproduction. We also discuss upscaling techniques that offer promising solutions for being able to run these models efficiently over large spatial extents. Our aim is to provide directions for future research efforts and to illustrate the value of the DVM approach.     

Diffusion-driven instabilities and emerging spatial patterns in patchy landscapes

Spatial variation in population densities across a landscape is a feature of many ecological systems, from self-organised patterns on mussel beds to spatially restricted insect outbreaks. It occurs as a result of environmental variation in abiotic factors and/or biotic factors structuring the spatial distribution of populations. However the ways in which abiotic and biotic factors interact to determine the existence and nature of spatial patterns in population density remain poorly understood. Here we present a new approach to studying this question by analysing a predator–prey patch-model in a heterogenous landscape. We use analytical and numerical methods originally developed for studying nearest-neighbour (juxtacrine) signalling in epithelia to explore whether and under which conditions patterns emerge. We find that abiotic and biotic factors interact to promote pattern formation. In fact, we find a rich and highly complex array of coexisting stable patterns, located within an enormous number of unstable patterns. Our simulation results indicate that many of the stable patterns have appreciable basins of attraction, making them significant in applications. We are able to identify mechanisms for these patterns based on the classical ideas of long-range inhibition and short-range activation, whereby landscape heterogeneity can modulate the spatial scales at which these processes operate to structure the populations.     

Coexistence of microbial populations and autostabilization of regulating factors in continuous culture: theory and experiments

The paper summarizes the author’s theoretical and experimental researches aimed at studying the rule of stable coexistence of interacting microbial populations within same trophic level. Populations of yeast and algae interact in open continuous cultures through regulating factors (RFs), which come together by the ability to be released or taken up by a microbial population and affect the growth of this and other population. Theoretical and experimental studies show that in steady state, the number of coexisting species is not greater than the number of RFs. Two-dimensional regions with different resultant species compositions of experimental equilibrium communities are plotted in the coordinates of “input levels of RFs”. This is perhaps the first study showing that the background steady-state levels of RFs in the system are not related to their input levels. This effect has been termed autostabilization of RFs, and its theoretical basis has been developed. A new criterion of intra- and inter-population microbial interactions has been introduced for RFs—growth acceleration response to a change in population density. Based on the proposed new criterion, experimental and theoretical estimates of the intensity and the sign of interactions between populations are given, allowing the quantification of their complex relationships, which was earlier unattainable. An integrated approach to detection of RFs has been proposed based on this criterion and the autostabilization effect.     

A methodology for evaluation of parent-mutant competition using a generalized non-linear ecosystem model

A generalized, non-linear population dynamics model of an ecosystem is used to investigate the direction of selective pressures upon a mutant by studying the competition between parent and mutant populations. The model has the advantages of considering selection as operating on the phenotype, of retaining the interaction of the mutant population with the ecosystem as a whole, and of setting a reasonable balance between theoretical manageability and quantitative testability.The model is used to make generalizations about several aspects of evolution in a terminal consumer (e.g. top carnivore). Mutations which avoid over exploitation of the prey species or introduce intra-specific population regulators will be selected against.Application of the method is limited primarily by the assumptions of a non-fluctuating environment, an asymptotically stable steady state before the mutation, absence of genetic drift, and uniformity of selective pressure on the mutant population.The method described in this paper is well suited for handling the high degree of complexity experienced in most ecosystems. Thus the methodology present may become a powerful tool in the approach to certain evolutionary questions.     

Hierarchical Bayesian Estimation for the Number of Species

This paper is concerned with the estimation of the number of species in a population through a fully hierarchical Bayesian model using the Metropolis algorithm. The proposed Bayesian estimator is based on Poisson random variables with means that are distributed according to some prior distributions with unknown hyperparameters. An empirical Bayes approach is considered and compared with the fully Bayesian approach based on biological data.     

Development and applications of a model for cellular response to multiple chemotactic cues

The chemotactic response of a cell population to a single chemical species has been characterized experimentally for many cell types and has been extensively studied from a theoretical standpoint. However, cells frequently have multiple receptor types and can detect and respond chemotactically to more than one chemical. How these signals are integrated within the cell is not known, and we therefore adopt a macroscopic phenomenological approach to this problem. In this paper we derive and analyze chemotactic models based on partial differential (chemotaxis) equations for cell movement in response to multiple chemotactic cues. Our derivation generalizes the approach of Othmer and Stevens [29], who have recently developed a modeling framework for studying different chemotactic responses to a single chemical species. The importance of such a generalization is illustrated by the effect of multiple chemical cues on the chemotactic sensitivity and the spatial pattern of cell densities in several examples. We demonstrate that the model can generate the complex patterns observed on the skin of certain animal species and we indicate how the chemotactic response can be viewed as a form of positional indicator. Received: 15 February 1999 / Revised version: 1 February 2000 / Published online: 14 September 2000     

MetaPopGen: an r package to simulate population genetics in large size metapopulations

Population genetics simulation models are useful tools to study the effects of demography and environmental factors on genetic variation and genetic differentiation. They allow for studying species and populations with complex life histories, spatial distribution and many other complicating factors that make analytical treatment impracticable. Most simulation models are individual‐based: this poses a limitation to simulation of very large populations because of the limits in computer memory and long computation times. To overcome these limitations, we propose an intermediate approach that allows modelling of very complex demographic scenarios, which would be intractable with analytical models, and removes the limitations imposed by large population size, which affect individual‐based simulation models. We implement this approach in a software package for the r environment, MetaPopGen. The innovative concept of this approach with respect to the other population genetic simulators is that it focuses on genotype numbers rather than on individuals. Genotype numbers are iterated through time by using random number generators for appropriate probabilistic distributions to reproduce the stochasticity inherent to Mendelian segregation, survival, dispersal and reproduction. Features included in the model are age structure, monoecious and dioecious (or separate sexes) life cycles, mutation, dispersal and selection. The model simulates only one locus at a time. All demographic parameters can be genotype‐, sex‐, age‐, deme‐ and time‐dependent. MetaPopGen is therefore indicated to study large populations and very complex demographic scenarios. We illustrate the capabilities of MetaPopGen by applying it to the case of a marine fish metapopulation in the Mediterranean Sea.     

An integrative approach to understanding host–parasitoid population dynamics in real landscapes

Many of our advances regarding the spatial ecology of predators and prey have been attributed to research with insect parasitoids and their hosts. Host–parasitoid systems are ideal for spatial-ecological studies because of the small size of the organisms, the often discrete distribution of their resources, and the relative ease with which host mortality from parasitoids can be determined. We outline an integrated approach to studying host–parasitoid interactions in heterogeneous natural landscapes. This approach involves conducting experiments to obtain critically important information on dispersal and boundary behavior of the host and parasitoid, large-scale manipulations of landscape structure to reveal the impacts of landscape change on host–parasitoid interactions and temporal population dynamics, and the development of spatially realistic, behavior-based landscape models. The dividends from such an integrative approach are far reaching, as is illustrated in our research on the prairie planthopper Prokelisia crocea and its egg parasitoid Anagrus columbi that occurs in the tall-grass prairies of North America. Here, we describe the population structure of this system which is based on a long-term survey of planthoppers and parasitoids among host–plant patches. We also outline novel approaches to experimentally quantify and model the movement and boundary behavior of animals in general. The value of this information is revealed in a landscape-level field experiment that was designed to test predictions about how landscape change affects the spatial and temporal population dynamics of the host and parasitoid. Finally, with these empirical data as the foundation, we describe novel simulation models that are spatially realistic and behavior based. Drawing from this integrated approach and case study, we identify key research questions for the future.     

Morphological diversity of sockeye salmon (<Emphasis Type="Italic">Oncorhynchus nerka</Emphasis> (Walbaum)) of the Kamchatka River

A method based on morphological characters is proposed to evaluate biological diversity of a sockeye salmon population. In all samples, morphological diversity of males is greater than that of females. In addition, the inter-annual dynamics determined on the basis of separate morphological characters differs in individuals of different sex. The necessity of studying biological diversity for preserving population homeostasis is also considered.