Theoretical ecology as etiological from the start

The world’s leading environmental advisory institutions look to ecological theory and research as an objective guide for policy and resource management decision-making. In addition to various theoretical merits of doing so, it is therefore crucially important to clear up confusions about ecology’s conceptual foundations and to make plain the basic workings of inferential methods used in the science. Through discussion of key moments in the genesis of the theoretical branch of ecology, this essay elucidates a general heuristic role of teleological metaphor in ecological research and defuses certain enduring confusions and misguided criticisms of current work in ecology

Global stability and repulsion in autonomous Kolmogorov systems

Criteria are established for the global attraction, or global repulsion on a compact invariant set, of interior and boundary fixed points of Kolmogorov systems. In particular, the notions of diagonal stability and Split Lyapunov stability that have found wide success for Lotka-Volterra systems are extended for Kolmogorov systems. Several examples from theoretical ecology and evolutionary game theory are discussed to illustrate the results

Introduction to the Special Volume on "Ecology and Ecological Modeling in R"

The third special volume in the "Foometrics in R" series of the Journal of Statistical Software collects a number of contributions describing statistical methodology and corresponding implementations related to ecology and ecological modelling. The scope of the papers ranges from theoretical ecology and ecological modelling to statistical methodology relevant for data analyses in ecological applications.

Diversity, competition, extinction: the ecophysics of language change

As early indicated by Charles Darwin, languages behave and change very much like living species. They display high diversity, differentiate in space and time, emerge and disappear. A large body of literature has explored the role of information exchanges and communicative constraints in groups of agents under selective scenarios. These models have been very helpful in providing a rationale on how complex forms of communication emerge under evolutionary pressures. However, other patterns of large-scale organization can be described using mathematical methods ignoring communicative traits. These approaches consider shorter time scales and have been developed by exploiting both theoretical ecology and statistical physics methods. The models are reviewed here and include extinction, invasion, origination, spatial organization, coexistence and diversity as key concepts and are very simple in their defining rules. Such simplicity is used in order to catch the most fundamental laws of organization and those universal ingredients responsible for qualitative traits. The similarities between observed and predicted patterns indicate that an ecological theory of language is emerging, supporting (on a quantitative basis) its ecological nature, although key differences are also present. Here we critically review some recent advances lying and outline their implications and limitations as well as open problems for future research.Comment: 17 Pages. A review on current models from statistical Physics and Theoretical Ecology applied to study language dynamic

Evolution of theoretical ecology in last decades: why did individual-based modelling emerge

Mathematical models of classical theoretical ecology are state variable models. They use density of population as a state variable. Because such models posses equilibrium states and they are stable around them, classical theoretical ecology has been dominated by considerations about stability of ecological systems. Three factors observed in ecology in last decades had great influence on the gradual decline of the classical theoretical ecology: first one is development of evolutionary ecology and the stress it laid on individuals, the second one nonequlibrium way of thinking about dynamics of ecological systems and the third one various methodological doubts about application of difference and differential equations in ecology. Individual-based modeling has emerged as the result of this discussions. However, individual-based approach to modeling the dynamics of ecological systems has natural tendency to describe particular systems and to produce their detailed models. Much should be done in the future to solve general problems formulated by classical theoretical ecology using method of individual-based approach

Apparent interactions in community models: A challenge for theoretical ecology

The primary aim of this working paper is to challenge theoretical ecologists to clarify the intended status of any simple model that they use. If we restrict our attention to a subcommunity and treat it as if it were the full community, we need to invoke apparent interactions that incorporate the effects of unspecified or hidden variables as well as direct interactions. This position resembles that of previous discussions of apparent competition (Holt 1977), indirect effects (Lawlor 1979), and ecological abstraction (Schaffer 1981), but my definition of apparent interactions differs from those stated or implied by these authors. I advance a method for calculating apparent interactions that incorporates the effects of hidden variables in a way that most closely generates the observed population trajectories. My method shows the apparent attractions can be counterintuitive, which points to some fundamental ambiguities in theoretical ecology. These ambiguities arise if we use simple models, that is, ones with few components, when we are actually concerned with naturally variable observations drawn from systems with more components than we have explicitly modeled. We need to clarify whether we intend simple models to represent the processes that generated those observations, or whether they are merely redescriptions or summaries of those observations or, a third possibility, whether they are mathematical systems which are used to suggest how ecological systems might operate

Mutualism supports biodiversity when the direct competition is weak

A key question of theoretical ecology is which properties of ecosystems favour their stability and help maintaining biodiversity. This qu estion recently reconsid- ered mutualistic systems, generating intense controversy about the role of mutu- alistic interactions and their network architecture. Here we show analytically and verify with simulations that reducing the effective intersp ecific competition and the propagation of perturbations positively influences struct ural stability against envi- ronmental perturbations, enhancing persistence. Notewor thy, mutualism reduces the effective interspecific competition only when the direct interspecific competition is weaker than a critical value. This critical competition i s in almost all cases larger in pollinator networks than in random networks with the same connectance. Highly connected mutualistic networks reduce the propagation of e nvironmental perturba- tions, a mechanism reminiscent of MacArthur’s proposal tha t ecosystem complexity enhances stability. Our analytic framework rationalizes p revious contradictory re- sults, and it gives valuable insight on the complex relation ship between mutualism and biodiversity

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa