Theoretical Population Ecology Research Articles

ESA HISTORICAL RECORDS COMMITTEE (Established 1944) Newsletter

ESA HISTORICAL RECORDS COMMITTEE (Established 1944) Newsletter

Decoding symbiotic endogeneity: the stochastic input-output analysis of university-business-government alliances

The theoretical population ecology constructs of commensalism, parasitism, and amensalism are applied in an analysis of the Knowledge Cluster Initiative (KCI), a unique social experiment establishing university-business-government alliances for knowledge-intensive innovative clusters in Japan. An analysis based on multiple negative binomial regressions to confirm the interdependence of the triple helix variables revealed that new startup venture firms served as an input factor for filing new patents and developing new products. Although the ultimate goal of the KCI was to promote new startups from the university, the startups had a commensal effect on patents and new product development. The Japanese cluster creation policy encouraged academic participation and created a mutual effect of launching new university startups. The resulting increase in the number of university researchers promoted the establishment of new startups. These startups had commensal relationships with patent applications and new product development. Although the creation of new venture startups was the ultimate goal of the cluster promotion policy, the results of this study indicate that it was the universities that benefited from the new startups that commensally contributed to increasing quantities of alliance outputs such as patents and new products.

Alfred J. Lotka and the origins of theoretical population ecology

Ecology, being an interdisciplinary subject, has often advanced by borrowing perspectives and methods from numerous other disciplines. Of the many cross-disciplinary transfers that have contributed to the science of ecology, one unusual source of inspiration has been physical chemistry, which in a round-about way served to stimulate the development of population ecology in the 1920s. This transfer from physical chemistry into biology was the brainchild of Alfred James Lotka (1880–1949), a man of exceptional creativity and one of the fathers of what would later become theoretical population ecology. Alfred James Lotka, 1880–1949. Image courtesy of MetLife Archives. Lotka’s story has an ironic twist because he did not intend to contribute to the discipline of ecology. His goal was to create a new biological discipline called “physical biology,” by which he meant the “broad application of physical principles and methods in the contemplation of biological systems ” (1). The analogy between physical chemistry and physical biology lay in the idea that the same basic laws governed both kinds of systems. In both systems, all processes could be reduced to two kinds of changes: those involving exchanges of matter between the components of the system, and those involving exchanges of energy. In the chemical system the components were molecules. In the biological system the components were organisms plus the raw materials in their environment, and the exchanges of matter and energy took place through the web of food relationships, growth, and reproduction. Understanding these relationships mathematically was the goal of physical biology. Because of his attention to systems, Lotka’s approach was strongly ecological, but when he started his project in the early 20th century, ecology barely existed as a discipline and he did not think, even two decades later, that ecologists would be his main audience. Lotka received his undergraduate education in … [↵][1]1Email: sharon{at}jhu.edu. [1]: #xref-corresp-1-1

Commensalism: Formation of Innovation Clusters through University-Business-Government Alliances

To transcend the traditional dichotomy between competition and cooperation, the theoretical population ecology constructs of Commensalism, Parasitism, and Amensalism were applied to an analysis of ...

Population Growth and Demography of Common Loons in the Northern United States

ABSTRACT We used recent developments in theoretical population ecology to construct basic models of common loon (Gavia immer) demography and population dynamics. We parameterized these models using existing survival estimates and data from long‐term monitoring of loon productivity and abundance. Our models include deterministic, 2‐stage, density‐independent matrix models, yielding population growth‐rate estimates (λ) of 0.99 and 1.01 for intensively studied populations in our Wisconsin, USA, and New Hampshire, USA, study areas, respectively. Perturbation analysis of these models indicated that estimated growth rate is extremely sensitive to adult survival, as expected for this long‐lived species. Also, we examined 20 years of count data for the 2 areas and evaluated support for a set of count‐based models of population growth. We detected no temporal trend in Wisconsin, which would be consistent with fluctuation around an average equilibrium state but could also result from data limitations. For New Hampshire, the model set included varying formulations of density dependence and partitioning of stochasticity that were enabled by the annual sampling resolution. The best model for New Hampshire included density regulation of population growth and, along with the demographic analyses for both areas, provided insight into the possible importance of breeding habitat availability and the abundance of nonbreeding adults. Based on these results, we recommend that conservation organizations include nonbreeder abundance in common loon monitoring efforts and that additional emphasis be placed on identifying and managing human influences on adult loon survival.

Nonlinearities in mathematical ecology: Phenomena and models: Would we live in Volterra's world?

Presented is a critical survey of canonical nonlinear models in theoretical population ecology, namely single-species population, prey–predator, competition, migration within a metapopulation, and trophic chains. Various nonlinear effects, like hysteresis, structural instability, dissipative structures, dynamic chaos, etc., do exist in these models, but the problem how to detect these phenomena in real ecosystems is not yet solved. In the mathematics of nonlinear models, the central question is whether the simplest, i.e., Volterra-type, nonlinearity is sufficient to reproduce a variety of nonlinear phenomena in a given model or we need a more sophisticated formalism. Examples are considered where the Volterra models fail. Although fundamental physical principles, like, e.g., the mass conservation law, should work in ecology too, the ecological origin of the models often causes mathematical effects which are distinct from those in theoretical physics. For example, the trophic-chain model does reveal a kind of chaotic behaviour, but the “ecological strange attractor” occupies an intermediate position between Lorenz's and Feigenbaum's attractors; moreover, the phase volume of our system contracts, hence the system is dissipative (like a Lorenz's one) in spite of its matter conservation property. Nevertheless, when applied properly, physical concepts, like, e.g., the thermodynamic notion of exergy, give better insight both to the patterns of nonlinear ecosystem behaviour and to comparison of the patterns.

Spatial pattern formation facilitates eradication of infectious diseases.

Control of animal-born diseases is a major challenge faced by applied ecologists and public health managers. To improve cost-effectiveness, the effort required to control such pathogens needs to be predicted as accurately as possible. In this context, we reviewed the anti-rabies vaccination schemes applied around the world during the past 25 years.We contrasted predictions from classic approaches based on theoretical population ecology (which governs rabies control to date) with a newly developed individual-based model. Our spatially explicit approach allowed for the reproduction of pattern formation emerging from a pathogen's spread through its host population.We suggest that a much lower management effort could eliminate the disease than that currently in operation. This is supported by empirical evidence from historic field data. Adapting control measures to the new prediction would save one-third of resources in future control programmes.The reason for the lower prediction is the spatial structure formed by spreading infections in spatially arranged host populations. It is not the result of technical differences between models.Synthesis and applications. For diseases predominantly transmitted by neighbourhood interaction, our findings suggest that the emergence of spatial structures facilitates eradication. This may have substantial implications for the cost-effectiveness of existing disease management schemes, and suggests that when planning management strategies consideration must be given to methods that reflect the spatial nature of the pathogen–host system.

Extinction risk, coloured noise and the scaling of variance

The impact of temporally correlated fluctuating environments (coloured noise) on the extinction risk of populations has become a main focus in theoretical population ecology. In this study we particularly focus on the extinction risk in strongly correlated environments. Here, we found that, in contrast to moderate auto-correlation, the extinction risk was highly dependent on the process of noise generation, in particular on the method of variance scaling. Such scaling is commonly applied to avoid variance-driven biases when comparing the extinction risk under white and coloured noise. We show that for strong auto-correlation often-used scaling techniques lead to a high variability in the variances of the resulting time series and thus to deviations in the subsequent extinction risk. Therefore, we present an alternative scaling method that always delivers the target variance, even in the case of strong auto-correlation. In contrast to earlier techniques, our very intuitive method is not bound to auto-regressive processes but can be applied to all types of coloured noises. We strongly recommend our method to generate time series when the target of interest is the effect of noise colour on extinction risk not obscured by any variance effects.

Establishment Risks for Invasive Species

This article presents a quantitative methodology for evaluating the probability of invasive pest species establishing persistent populations. The estimation of pest establishment relies on data and information describing the biology and ecology of the pest and its interactions with potential host species and the regional environment. This information is developed using a model construct borrowed from theoretical population ecology. The methodology for estimating the probability of pest establishment is part of an overall framework that explores the implications of reductions in pest invasions on subsequent establishment. The risk reduction framework integrates the engineering aspects of different technologies for reducing pest entry, the biology and ecology of pest species, the suitability of potentially susceptible hosts, and the quality of available habitats. The methodology for estimating the risk of establishment is presented using an example pest, the Asian longhorned beetle (Anoplophora glabripennis), which has been introduced into the United States via solid wood packing materials (SWPM) used in international commerce. Uncertainties inherent to the estimation of model parameters that determine the risk of establishment are defined, quantified, and propagated through the population model. Advantages and limitations of the proposed methodology are discussed along with recommendations to make the approach more useful in the management of risks posed by the establishment of pest populations.

In Brief

At the World Association for the Advancement of Veterinary Parasitology (WAAVP) meeting in Stresa, Italy, Richard Wall (University of Bristol, UK) received the inaugural 2001 WAAVP Peter Nansen Young Scientist Award for his remarkable scientific accomplishments. Wall has made outstanding contributions to the field of veterinary parasitology, focusing on ecology, behavior and control of arthropod parasites. In particular, his integral approach, using theoretical population ecology and epidemiology, has resulted in novel and innovative control strategies with practical applications. The 2001 WAAVP Pfizer Award for outstanding research in veterinary parasitology was awarded to Roger Prichard (Institute of Parasitology, McGill University, Montreal, Canada). Prichard's research focuses on the mechanisms and genetics of resistance to antiparasitic drugs, parasite control and molecular biology of helminths. (Source: WAAVP Newsletter 2002, 5). TS

Individual variability and population regulation: a model of the significance of within-generation density dependence.

Most models of theoretical population ecology consider population density as a state variable and thus ignore the fact that populations are composed not of identical average individuals but of individuals which are usually different. However, this individual variability may be important for population regulation. We therefore analysed an individual-based population model which explicitly describes within-generation processes, i.e. individual growth, starvation, and resource dynamics. The results show that if population dynamics are dominated by slow changes in resource level, the population size in the model undergoes wide oscillation, often leading to extinction. If, on the other hand, fast within-generation processes predominate, such as starvation and sudden drops in resource levels, the population fluctuates to a limited extent around an average. Within-generation density dependence may thus be an important mechanism which is largely ignored in classic time-discrete state-variable models. We conclude that the individual-based approach provides important insights into the hierarchical organization of population dynamics, i.e. the relationship between fast processes at the individual level and slower processes at the population level.

Improved understanding of dispersal in crop pest and disease management: current status and future directions

Implementation of integrated pest management (IPM) is by definition scale- and location-specific and because these features govern biophysical processes such as dispersal, information on the latter is relevant for IPM. However, dispersal has not featured prominently as the defining and critical element of IPM schemes. At the larger regional or inter-continental scales then our understanding of dispersal has improved with the availability of new tracking technologies (e.g., Doppler weather radar, see Westbrook and Isard (1999)), but proportionate returns in terms of IPM have not been demonstrated. There is increasing interest in describing within-field and within-plant dispersal events, but much remains to be done to integrate research findings into operational IPM programmes. Models are often seen as useful tools for analysing pest and disease outbreaks and for the evaluation of alternative control options. Dispersal, in full biophysical detail, has rarely featured explicitly in such models, and indeed the lack of biological balance in complex simulation models places their uses in research, technology development or implementation somewhat in doubt. Often, however, it is possible to incorporate dispersal or more generally spatial processes into simplified models that combine applicability to real pest management situations with the insights of theoretical population ecology. Such insights are relevant to the multiple-species interactions characteristic of biological control and the ways in which dispersal affects the qualitative attributes of these interactions. Dispersal research has had a demonstrable impact on pest management and new opportunities will arise in the future.

Reconciling Classical and Individual‐Based Approaches in Theoretical Population Ecology: A Protocol for Extracting Population Parameters from Individual‐Based Models

The two main approaches in theoretical population ecology-the classical approach using differential equations and the approach using individual-based modeling-seem to be incompatible. Linked to these two approaches are two different timescales: population dynamics and behavior or physiology. Thus, the question of the relationship between classical and individual-based approaches is related to the question of the mutual relationship between processes on the population and the behavioral timescales. We present a simple protocol that allows the two different approaches to be reconciled by making explicit use of the fact that processes operating on two different timescales can be treated separately. Using an individual-based model of nomadic birds as an example, we extract the population growth rate by deactivating all demographic processes-in other words, the individuals behave but do not age, die, or reproduce. The growth rate closely matches the logistic growth rate for a wide range of parameters. The implications of this result and the conditions for applying the protocol to other individual-based models are discussed. Since in physics the technique of separating timescales is linked to some concepts of self-organization, we believe that the protocol will also help to develop concepts of self-organization in ecology.

Reconciling Classical and Individual-Based Approaches in Theoretical Population Ecology: A Protocol for Extracting Population Parameters from Individual-Based Models

The two main approaches in theoretical population ecology—the classical approach using differential equations and the approach using individual‐based modeling—seem to be incompatible. Linked to these two approaches are two different timescales: population dynamics and behavior or physiology. Thus, the question of the relationship between classical and individual‐based approaches is related to the question of the mutual relationship between processes on the population and the behavioral timescales. We present a simple protocol that allows the two different approaches to be reconciled by making explicit use of the fact that processes operating on two different timescales can be treated separately. Using an individual‐based model of nomadic populabirds as an example, we extract the population growth rate by deactivating all demographic processes—in other words, the individuals behave but do not age, die, or reproduce. The growth rate closely matches the logistic growth rate for a wide range of parameters. The implications of this result and the conditions for applying the protocol to other individual‐based models are discussed. Since in physics the technique of separating timescales is linked to some concepts of self‐organization, we believe that the protocol will also help to develop concepts of self‐organization in ecology.

Of mites and models: an introduction to the symposium proceedings

It is most remarkable that the study of one group of arthropods, the mites, has generated a disproportionately large contribution to recent progress in the area of theoretical population ecology, and hence, in the development of a theoretical foundation for pest management. It is probably a combination of various factors that has brought this situation about. Mites are very small organisms, allowing the all important large sample sizes, even in laboratory studieS, yet, they are large enough to be individually noted and manipulated. The fact that they are terrestrial, flightless surface dwellers simplifies observing, counting and model testing, and alleviates the problem of unwanted dispersal. That several phytophagous mites are major agricultural pests, and that many of their natural enemies are also mites certainly encourages researchers to choose these organisms as their subjects of consideration. Perhaps the most significant development in furthering our understanding of animal population dynamics is our coming to terms with rejecting the assumption of homogeneity in populations. Starting in the 1960s several authors stressed the importance of spatial heterogeneity, scale, patchiness, space and time lags, and other such concepts that challenged the assumption of homogeneity (e.g. Den Boer, 1968; Maynard Smith, 1974; Roff, 1974; Hilborn, 1975; Hastings, 1977; Crowley, 1978 ), but little formal theory or experimental work addressed this aspect of ecology until the early 1980s. The mite researchers and theoretical ecologists alike, Huffaker's (1958) population experiments with an orange-feeding spider mite and its phytoseiid predator are generally recognized as a milestone in challenging the homogeneity assumption. These showed that prey and predator were able to coexist for a longer period in a more heterogeneous environment than in a more homogeneous one. This observation has been widely cited as the first demonstration that prey-predator persistence is enhanced by patchiness in the environment (see Taylor, 1991 ). However, Huffaker's results were more complex than a mere relation between complexity and persistence. One of the most intriguing features of Huffaker's prey-predator dynamics was the occurrence of distinct cycles. This extra dimension has received a lot of attention (e.g. Maynard

Defining ecology: Ecological theories, mathematical models, and applied biology in the 1960s and 1970s

Ever since the early decades of this century, there have emerged a number of competing schools of ecology that have attempted to weave the concepts underlying natural resource management and natural-historical traditions into a formal theoretical framework. It was widely believed that the discovery of the fundamental mechanisms underlying ecological phenomena would allow ecologists to articulate mathematically rigorous statements whose validity was not predicated on contingent factors. The formulation of such statements would elevate ecology to the standing of a rigorous scientific discipline on a par with physics. However, there was no agreement as to the fundamental units of ecology. Systems ecologists sought to identify the fundamental organization that tied the physical and biological components of ecosystems into an irreducible unit: the ecosystem was their fundamental unit. Population ecologists sought, instead, to identify the biological mechanisms regulating the abundance and distribution of plant and animal species: to these ecologists, the individual organism was the fundamental unit of ecology, and the physical environment was nothing more than a stage upon which the play of individuals in perennial competition took place. As Joel Hagen has pointed out, the two schools were thus dividied by fundamentally different and irreconcilable assumptions about the nature of ecosystems. Notwithstanding these divisive efforts to elevate the image of ecology, the discipline remained in the shadows of American academia until the mid-1960s, when systems ecologists succeeded in projecting ecology onto the national scene. They did so by seeking closer involvement with practical problems: they argued before Congress that their approach to the theoretical problems of ecology was uniquely suited to the solution of the impending “environmental crisis.” With the establishment of the International Biological Program, they succeeded in attracting unprecedented levels of funding for systems ecology research. Theoretical population ecologists, on the other hand, found themselves consigned to the outer regions of this new institutional landscape. The systems ecologists' successful capture of the limelight and the purse brought the divisions between them and population ecologists into sharper relief — hence the hardening of the division of ecology observed by Hagen.45 I have argued that the population biologist Richard Levins, prompted by these institutional developments, sought to challenge the social position of systems ecology, and to assert the intellectual priority of theoretical population ecology. He attempted to do so by articulating a nontrivial and rather carefully thought out classification of ecological models that led to the disqualification of systems analysis as a legitimate approach to the study of ecological phenomena. I have suggested that — ultimately —Levins's case against systems analysis in ecology rested on the view that an aspiration to realism and prediction was incompatible with an interest in theoretical issues, a concern that he equated with the search for generality. He sought to reinforce this argument by exploiting the fact that systems ecologists had staked their future on the provision of technical solutions to the problems of the “environmental crisis”: he associated systems ecologists' aspiration to realism and precision with a concern for practical issues, trading on the widely accepted view that practical imperatives are incompatible with the aims of scientific inquiry.46 These are plausible, but nonetheless questionable, claims which have now become an integral part of ecological knowledge. And finally, I hope to have shown how even the most abstract levels of scientific argument are shaped by political considerations, and how discussions of the conceptual development of modern ecology might benefit from a greater consideration of its historical and social dimensions.47