Food web models: a plea for groups

The concept of a group is ubiquitous in biology. It underlies classifications in evolution and ecology, including those used to describe phylogenetic levels, the habitat and functional roles of organisms in ecosystems. Surprisingly, this concept is not explicitly included in simple models for the structure of food webs, the ecological networks formed by consumer–resource interactions. We present here the simplest possible model based on groups, and show that it performs substantially better than current models at predicting the structure of large food webs. Our group-based model can be applied to different types of biological and non-biological networks, and for the first time merges in the same framework two important notions in network theory: that of compartments (sets of highly interacting nodes) and that of roles (sets of nodes that have similar interaction patterns). This model provides a basis to examine the significance of groups in biological networks and to develop more accurate models for ecological network structure. It is especially relevant at a time when a new generation of empirical data is providing increasingly large food webs.     

竞争进化与协同进化

竞争和协同作用是普遍存在于生物个体或种群之间的两种表现行为。大量实验和研究表明,竞争主导的生物进化是存在的,在一定范围和水平上竞争的结果有利于植物形态、生理适应特征及生活史适应策略的进化。协同能够使生物以最小的代价或成本实现自身在自然界的存在与繁殖(最大适合度);基于生态系统的稳定性和生物多样性的角度考虑,与竞争相辅相成、在一定条件下可以相互转化的协同作用更有利于生态系统各组分之间能量转化效率的提高,有利于加强系统自身的自组织能力,有利于维持生态系统的有序性和多样性。因此,协同作用的结果应该是更有利于生物进化,而且比竞争更普遍、更有意义。     

Habitat modeling with single- and multi-target trees and ensembles

Habitat modeling studies the influence of abiotic factors on the abundance of a given taxonomic group of organisms. In this work, we investigate the effect of environmental conditions on communities of organisms in three different ecosystems. Namely, we consider the diatom community in Lake Prespa, Macedonia, the Collembola community in the soils of Denmark and 14 organisms living in Slovenian rivers. The data for these case studies consist of physical and chemical properties of the environment as well as the relative abundances or presence of the organisms under investigation.The multi-species data are analyzed by constructing habitat models for each species separately (single-target decision trees) or by constructing a single habitat model for all the species (multi-target predictive clustering trees). Typically, habitat models are constructed for each species individually and thus do not exploit the interactions between/among species. While approaches for building a single habitat model of a group of organisms exist, they typically construct models that are not readily interpretable and, thus, are seldom used by the research community. In this work, we explore in detail the construction of interpretable models of both types. Furthermore, we construct ensembles of decision trees and ensembles of predictive clustering trees to increase the predictive performance of the models.The key outcomes of the interpretation and discussion of the obtained models for each case study are as follows. First, we show that multi-target predictive clustering trees are a very useful method for the analysis of multi-species data and that they are more efficient and produce more concise models than single-target decision trees. The obtained multi-target habitat models are readily interpretable and identify the environmental conditions that influence the composition and structure of a given community of organisms. Second, we conclude that the temperature and magnesium are the most important factors influencing the complete diatom community in Lake Prespa, while the nitrates and the temperature influence more the most abundant species. Third, the biological oxygen demand is the most influential factor for the abundance of river dwelling species, while the river community structure is mostly influenced by the NO₂ concentration. Finally, the structure of the community of soil microarthropods is mostly influenced by the soil type and the crop history.     

Signatures of nitrogen limitation in the elemental composition of the proteins involved in the metabolic apparatus

Nitrogen (N) is a fundamental component of nucleotides and amino acids and is often a limiting nutrient in natural ecosystems. Thus, study of the N content of biomolecules may establish important connections between ecology and genomics. However, while significant differences in the elemental composition of whole organisms are well documented, how the flux of nutrients in the cell has shaped the evolution of different cellular processes remains poorly understood. By examining the elemental composition of major functional classes of proteins in four multicellular eukaryotic model organisms, we find that the catabolic machinery shows substantially lower N content than the anabolic machinery and the rest of the proteome. This pattern suggests that ecological selection for N conservation specifically targets cellular components that are highly expressed in response to nutrient limitation. We propose that the RNA component of the anabolic machineries is the mechanistic force driving the elemental imbalance we found, and that RNA functions as an intracellular nutrient reservoir that is degraded and recycled during starvation periods. A comparison of the elemental composition of the anabolic and catabolic machineries in species that have experienced different levels of N limitation in their evolutionary history (animals versus plants) suggests that selection for N conservation has preferentially targeted the catabolic machineries of plants, resulting in a lower N content of the proteins involved in their catabolic processes. These findings link the composition of major cellular components to the environmental factors that trigger the activation of those components, suggesting that resource availability has constrained the atomic composition and the molecular architecture of the biotic processes that enable cells to respond to reduced nutrient availability.     

Local adaptation to biocontrol agents: A multi-objective data-driven optimization model for the evolution of resistance

Spatial and temporal variability in the application of biological control agents such as parasites or pathogenic bacteria can cause the evolution of resistance in pest organisms. Because biocontrol will be more effective if organisms are not resistant, it is desirable to examine the evolution of resistance under different application strategies.We present a computational method that integrates a genetic algorithm with experimental data for predicting when local populations are likely to evolve resistance to biocontrol pathogens. The model incorporates parameters that can be varied as part of pest control measures such as the distribution and severity of the biocontrol agent (e.g., pathogenic fungi). The model predicts the evolution of pathogen defense as well as indirect selection on several aspects of the organism's genetic system. Our results show that both variability of selection within populations as well as mean differences among populations are important in the evolution of defenses against biocontrol pathogens. The mean defense is changed through the pest organism's genotype and the variance is affected by components of the genetic system, namely, the resiliency, recombination rate and number of genes.The data-driven model incorporates experimental data on pathogen susceptibility and the cost of defense. The results suggest that spatial variability rather than uniform application of biological control will limit the evolution of resistance in pest organisms.     

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.     

The Origin of Cellular Life and Biosemiotics

Recent successes of systems biology clarified that biological functionality is multilevel. We point out that this fact makes it necessary to revise popular views about macromolecular functions and distinguish between local, physico-chemical and global, biological functions. Our analysis shows that physico-chemical functions are merely tools of biological functionality. This result sheds new light on the origin of cellular life, indicating that in evolutionary history, assignment of biological functions to cellular ingredients plays a crucial role. In this wider picture, even if aggregation of chance mutations of replicator molecules and spontaneously self-assembled proteins led to the formation of a system identical with a living cell in all physical respects but devoid of biological functions, it would remain an inanimate physical system, a pseudo-cell or a zombie-cell but not a viable cell. In the origin of life scenarios, a fundamental circularity arises, since if cells are the minimal units of life, it is apparent that assignments of cellular functions require the presence of cells and vice versa. Resolution of this dilemma requires distinguishing between physico-chemical and biological symbols as well as between physico-chemical and biological information. Our analysis of the concepts of symbol, rule and code suggests that they all rely implicitly on biological laws or principles. We show that the problem is how to establish physico-chemically arbitrary rules assigning biological functions without the presence of living organisms. We propose a solution to that problem with the help of a generalized action principle and biological harnessing of quantum uncertainties. By our proposal, biology is an autonomous science having its own fundamental principle. The biological principle ought not to be regarded as an emergent phenomenon. It can guide chemical evolution towards the biological one, progressively assigning greater complexity and functionality to macromolecules and systems of macromolecules at all levels of organization. This solution explains some perplexing facts and posits a new context for thinking about the problems of the origin of life and mind.     

Saccharomyces cerevisiae as a model organism: a comparative study

Background

Model organisms are used for research because they provide a framework on which to develop and optimize methods that facilitate and standardize analysis. Such organisms should be representative of the living beings for which they are to serve as proxy. However, in practice, a model organism is often selected ad hoc, and without considering its representativeness, because a systematic and rational method to include this consideration in the selection process is still lacking.

Methodology/Principal Findings

In this work we propose such a method and apply it in a pilot study of strengths and limitations of Saccharomyces cerevisiae as a model organism. The method relies on the functional classification of proteins into different biological pathways and processes and on full proteome comparisons between the putative model organism and other organisms for which we would like to extrapolate results. Here we compare S. cerevisiae to 704 other organisms from various phyla. For each organism, our results identify the pathways and processes for which S. cerevisiae is predicted to be a good model to extrapolate from. We find that animals in general and Homo sapiens in particular are some of the non-fungal organisms for which S. cerevisiae is likely to be a good model in which to study a significant fraction of common biological processes. We validate our approach by correctly predicting which organisms are phenotypically more distant from S. cerevisiae with respect to several different biological processes.

Conclusions/Significance

The method we propose could be used to choose appropriate substitute model organisms for the study of biological processes in other species that are harder to study. For example, one could identify appropriate models to study either pathologies in humans or specific biological processes in species with a long development time, such as plants.     

Mathematical modeling of colony formation in algal blooms: phenotypic plasticity in cyanobacteria

In this paper, we analyzed a mathematical model of algal-grazer dynamics, including the effect of colony formation, which is an example of phenotypic plasticity. The model consists of three variables, which correspond to the biomasses of unicellular algae, colonial algae, and herbivorous zooplankton. Among these organisms, colonial algae are the main components of algal blooms. This aquatic system has two stable attractors, which can be identified as a zooplankton-dominated (ZD) state and an algal-dominated (AD) state, respectively. Assuming that the handling time of zooplankton on colonial algae increases with the colonial algae biomass, we discovered that bistability can occur within the model system. The applicability of alternative stable states in algae-grazer dynamics as a framework for explaining the algal blooms in real lake ecosystems, thus, seems to depend on whether the assumption mentioned above is met in natural circumstances.     

Imitation model of 90Sr behaviour in the soil and stand of pine forest

The algorithm of display of 90Sr behaviour mechanisms in forest ecosystems by method of imitating modeling is developed. Distinctive features of algorithm: the 90Sr contents in vegetation is subdivided into two parts (outside and internal pollution), which dynamics is considered separately; dynamics of a radionuclide is considered in connection with dynamics of organic substance; it is supposed, that 90Sr behaviour in plants is similar to Ca behaviour; the biological availability 90Sr contained in a soil, is integrated function of time and physico-chemical properties of the given soil. On the basis of offered algorithm the model is constructed which is used for realization of a number of numerical experiments, including reconstruction of a situation of pollution of forest ecosystem on grey forest soils in result of Kyshtym accident. The quantitative estimations of intensity of 90Sr redistribution between stand components and soil are received. The modern problems of creation of prognostication models of 90Sr dynamics in the forest ecosystems are discussed.     

Evolution of complexity in RNA-like replicator systems

Background  

The evolution of complexity is among the most important questions in biology. The evolution of complexity is often observed as the increase of genetic information or that of the organizational complexity of a system. It is well recognized that the formation of biological organization – be it of molecules or ecosystems – is ultimately instructed by the genetic information, whereas it is also true that the genetic information is functional only in the context of the organization. Therefore, to obtain a more complete picture of the evolution of complexity, we must study the evolution of both information and organization.     

A novel modeling approach for the “end-to-end” analysis of marine ecosystems

There is a growing demand for “end-to-end” models, which are modeling tools used to analyze and understand the fundamental complexities of marine ecosystems and processes emerging from the interaction of individuals from different trophic groups with respect to the physical environment and, even, human activity. These models are valuable quantitative tools for ecosystem-based management. To explore potential answers to complex questions regarding ecosystems using these models, it is necessary to incorporate classical ontogenic changes through the life cycle of target individuals, in addition to inherited behavioral strategies, as an additional differentiating aspect, particularly when the behavior has a direct impact on the ecosystem phenomena under study. However, it is difficult to combine different fine scale time and spatial granularities to infer animal behavior and ontogenic development. This complexity has kept these two levels of analysis separated, because most current tools do not have the required computational resources and advanced software architecture. To address this issue, we propose an individual-based modeling framework that is capable of handling and unifying the two experimental categories with a comprehensive biological and behavioral model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms. In addition, this model incorporates the exchange and transfer of mass and energy through local interactions at all trophic levels (lower to higher), the physical environment, and anthropogenic activity. For the framework to model short time events, such as classical predator–prey interactions, while also generating long-term ecosystem emergent properties, a special interleaving scheduling engine and physical space computer model was devised, which optimizes memory and processing resources. The framework was tested through several experiments with a three-population ecosystem containing up to 40 thousand organisms evolving inside a 200,000 m² simulation environment during 12,000 model-hours; yet, requiring only a few hours of program execution on a regular personal computer. The model included various environmental physical elements, such as several hundred shelters, the number of which can be easily modified in each experiment to simulate substrate degradation and its impact on populations. With the aid of the quantitative and qualitative tools provided by the model, it was possible to observe a coupling between prey and predator population dynamics. In conclusion, we confirmed that the end-to-end model developed here could successfully generate detailed specific hypotheses about fish behavior and quantify impacts on population dynamics.     

Investigating the evolution and development of biological complexity under the framework of epigenetics

Biological complexity is a key component of evolvability, yet its study has been hampered by a focus on evolutionary trends of complexification and inconsistent definitions. Here, we demonstrate the utility of bringing complexity into the framework of epigenetics to better investigate its utility as a concept in evolutionary biology. We first analyze the existing metrics of complexity and explore the link between complexity and adaptation. Although recently developed metrics allow for a unified framework, they omit developmental mechanisms. We argue that a better approach to the empirical study of complexity and its evolution includes developmental mechanisms. We then consider epigenetic mechanisms and their role in shaping developmental and evolutionary trajectories, as well as the development and organization of complexity. We argue that epigenetics itself could have emerged from complexity because of a need to self‐regulate. Finally, we explore hybridization complexes and hybrid organisms as potential models for studying the association between epigenetics and complexity. Our goal is not to explain trends in biological complexity but to help develop and elucidate novel questions in the investigation of biological complexity and its evolution.     

Models of cultural niche construction with selection and assortative mating

Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.     

The role of dissolved organic matter, particularly free amino acids and humic substances, in freshwater ecosystems

1. Although the mass of dissolved organic matter (DOM) often exceeds that of living organisms in freshwaters, little is known about the roles of its constituent molecules as sources of energy and information for aquatic organisms. In the present review attention is focused on free amino acids (FAA) and humic substances (HS) as examples of labile and refractory components within DOM. 2. The following questions are addressed. (i) What are spatiotemporal patterns in the distribution of DOM, HS and FAA? (ii) What are the origins of the components of DOM and how are their concentrations regulated? (iii) What is the significance of the spatial and temporal distributional patterns of DOM, HS and FAA to detritivorous invertebrates and other organisms associated with them? (iv) What is the relevance of DOM to the food web concept and to the biochemical ecology of freshwater ecosystems? 3. Concentrations of DOM, FAA and HS within lentic ecosystems are ranked as follows: Sediment pore water > Air–water interface > Midwater column. Comparisons between water bodies show that the concentrations of labile constituents of DOM, such as FAA, are usually positively correlated with base cations, nutrients and biological activity. In contrast, HS concentrations are negatively correlated with base cations or nutrients but positively correlated with the rate of biological degradation (the maximum values occurring in the autumn). The FAA : HS ratios might serve therefore as an indicator of the potential productivity of a water body. 4. External sources of DOM in general, and FAA and HS in particular, include rainwater, windborne material, surface flow and groundwater. The relative importance of these allochthonous sources of DOM decreases along the length of lotic ecosystems and also with increase in size of lentic ecosystems. Internal sources of FAA and HS include synthesis or polymerization from existing organic matter, degradation of organic matter and release from both living and dead organisms. The net accumulation of DOM released by living bacteria, phytoplankton, epilithon, macrophytes and invertebrates is much reduced due to heterotrophic uptake. Hence, most of the allochthonous DOM in freshwater originates from dead organic matter deposited on the sediment. Phytoplankton-dominated ecosystems may, however, differ, as most of their DOM may be recycled within the water column. 5. The factors that determine the external concentrations of DOM, FAA and HS are discussed. Evidence is cited in support of the following testable hypotheses. (i) The rates of production of DOM components will be favoured by increasing base cation and nutrient concentrations. (ii) Colloidal clay, base cations, biopolymers and living organisms, particularly bacteria, facilitate the removal of HS. Consequently, base-rich eutrophic waters tend to have lower HS concentrations than oligotrophic, base-deficient waters. (iii) As a result of higher productivity and selective removal of FAA, eutrophic waters tend to have higher FAA concentrations than those that are oligotrophic. 6. Labile DOM components, such as FAA, act as sources of information for aquatic organisms. More research is needed in this field. There is a consensus that DOM acts as an important source of energy for aquatic bacteria, thus forming the microbial loop. However, higher eukaryotic organisms also utilize DOM, including components released by bacteria and plants as metabolic end-products and photoassimilates, respectively. As a result, these DOM components may be more important as food for macrodecomposers than the microdecomposers themselves. HS may also benefit aquatic organisms by promoting their growth and protecting them from inimical forces. Conversely, the removal of photons and the release of toxins by HS may be detrimental to aquatic organisms. 7. It is concluded that the central dogma of the foodweb, and its implicit assumption that the energy flow in aquatic ecosystems can be quantified solely by measuring rates of photosynthesis, ingestion of solid food and its digestion by higher organisms, is invalid. To extend our understanding of the role of DOM as a source of nutrition and information to aquatic organisms it is suggested that the subject should be studied within the context of ‘modules’ which have the following properties: (i) the components have co-evolved; (ii) the more vulnerable components will have protective mechanisms; (iii) the components will derive mutual benefits from co-existence; (iv) sedentary components will release kairomonal attractants or developmental primers; (v) living components will exchange energy and information; (vi) the module will collapse following the removal of strongly interactive keystone species. An example of a three-component, three-subset module, is provided by tubificid worms, epilithic bacteria and algae. A more complex module consisting of pulmonate snails, associated macrophytes, their epiphytic bacteria and algae has four components and six subsets. The elucidation of the interactive mechanisms within such modules demands an interdisciplinary approach, involving microbiology, biochemistry and behavioural biology.     

溪流粗木质残体的生态学研究进展

粗木质残体（CWD）是森林或溪流生态系统中残存的超过一定直径大小的站杆、倒木、枝桠及根系等死木质物的总称,溪流CWD对于溪流生态系统的稳定,水生生物多样性,河槽形态及其变化过程有着重要的作用。对溪流CWD的产生和分类,溪流CWD对于溪流生态系统的稳定,水生生物多样性,河槽形态及其变化过程有着重要的作用。对溪流CWD的产生和分类,溪流CWD贮量,分布和动态,以及溪流CWD的功能和管理分别进行了总结,并指出应尽快在国内开展溪流CWD的研究和管理。