Population as an oscillating system

In this paper we present a new approach describing population dynamics based on the view of a population as an oscillating system. To develop a mathematical model of an oscillating population, we applied a third-order differential equation. Our model describes population dynamics within a parametric-temporal continuum, formed by the relative values of population growth and decrease over time. In this paper we also illustrate how our oscillative model effectively compliments the existing suite of models in population dynamics.     

Sampling variability and estimates of density dependence: a composite-likelihood approach

It is well known that sampling variability, if not properly taken into account, affects various ecologically important analyses. Statistical inference for stochastic population dynamics models is difficult when, in addition to the process error, there is also sampling error. The standard maximum-likelihood approach suffers from large computational burden. In this paper, I discuss an application of the composite-likelihood method for estimation of the parameters of the Gompertz model in the presence of sampling variability. The main advantage of the method of composite likelihood is that it reduces the computational burden substantially with little loss of statistical efficiency. Missing observations are a common problem with many ecological time series. The method of composite likelihood can accommodate missing observations in a straightforward fashion. Environmental conditions also affect the parameters of stochastic population dynamics models. This method is shown to handle such nonstationary population dynamics processes as well. Many ecological time series are short, and statistical inferences based on such short time series tend to be less precise. However, spatial replications of short time series provide an opportunity to increase the effective sample size. Application of likelihood-based methods for spatial time-series data for population dynamics models is computationally prohibitive. The method of composite likelihood is shown to have significantly less computational burden, making it possible to analyze large spatial time-series data. After discussing the methodology in general terms, I illustrate its use by analyzing a time series of counts of American Redstart (Setophaga ruticilla) from the Breeding Bird Survey data, San Joaquin kit fox (Vulpes macrotis mutica) population abundance data, and spatial time series of Bull trout (Salvelinus confluentus) redds count data.     

Identifying key species in ecosystems with stochastic sensitivity analysis

The development of approaches to estimate the vulnerability of biological communities and ecosystems to extirpations and reductions of species is a central challenge of conservation biology. One key aim of this challenge is to develop quantitative approaches to estimate and rank interaction strengths and keystoneness of species and functional groups, i.e. to quantify the relative importance of species. Network analysis can be a powerful tool for this because certain structural aspects of ecological networks are good indicators of the mechanisms that maintain co-evolved, biotic interactions. A static view of ecological networks would lead us to focus research on highly-central species in food webs (topological key players in ecosystems). There are a variety of centrality indices, developed for several types of ecological networks (e.g. for weighted and un-weighted webs). However, truly understanding extinction and its community-wide effects requires the use of dynamic models. Deterministic dynamic models are feasible when population sizes are sufficiently large to minimize noise in the overall system. In models with small population sizes, stochasticity can be modelled explicitly. We present a stochastic simulation-based ecosystem model for identification of “dynamic key species” in situations where stochastic models are appropriate. To demonstrate this approach, we simulated ecosystem dynamics and performed sensitivity analysis using data from the Prince William Sound, Alaska ecosystem model. We then compare these results to those of purely topological analyses and deterministic dynamic (Ecosim) studies. We present the relationships between various topological and dynamic indices and discuss their biological relevance. The trophic group with the largest effect on others is nearshore demersals, the species mostly sensitive to others is halibut, and the group of both considerable effect on and sensitivity to others is juvenile herring. The most important trophic groups in our dynamical simulations appear to have intermediate trophic levels.     

Temperature as a key driver of ecological sorting among invasive pest species in the tropical Andes

Invasive species are a major threat to the sustainable provision of ecosystem products and services, both in natural and agricultural ecosystems. To understand the spatial arrangement of species successively introduced into the same ecosystem, we examined the tolerance to temperature and analyzed the field distribution of three potato tuber moths (PTM, Lepidoptera: Gelechiidae), that were introduced in Ecuador since the 1980s. We studied physiological responses to constant temperatures of the three PTM species under laboratory conditions and modeled consequences for their overall population dynamics. We then compared our predictions to field abundances of PTM adults collected in 42 sites throughout central Ecuador. Results showed that the three PTM species differed with respect to their physiological response to temperature. Symmetrischema tangolias was more cold tolerant while Tecia solanivora had the highest growth rates at warmer temperatures. Phthorimaea operculella showed the poorest physiological performance across the range of tested temperatures. Overall, field distributions agree with predictions based on physiological experiments and life table analyses. At elevations >3000 m, the most cold-tolerant species, S. tangolias, was typically dominant and often the only species present. This species may therefore represent a biological sensor of climate change. At low elevations (<2700 m), T. solanivora was generally the most abundant species, probably due to its high fecundity at high temperatures. At mid elevations, the three species co-occurred, but P. operculella was generally the least abundant species. Consistent with these qualitative results, significant regression analyses found that the best predictors of field abundance were temperature and a species x temperature interaction term. Our results suggest that the climatic diversity in agricultural landscapes can directly affect the community composition following sequential invasions. In the tropical Andes, as in other mountain ecosystems, the wide range of thermal environments found along elevational gradients may be one reason why the risks of invasion by successively introduced pest species could increase in the near future. More data on potential biological risks associated with climatic warming trends in mountain systems are therefore urgently needed, especially in developing nations where such studies are lacking.     

Effects of Social Structure and Prey Dynamics on Extinction Risk in Gray Wolves

Extinction models based on diffusion theory generally fail to incorporate two important aspects of population biology—social structure and prey dynamics. We include these aspects in an individual-based extinction model for small, isolated populations of the gray wolf (Canis lupus). Our model predicts mean times to extinction significantly longer than those predicted by more general (diffusion) models. According to our model, an isolated population of 50 wolves has a 95% chance of surviving just 9 years and only a 30% chance of surviving beyond 100 years. Reflecting the influence of social structure, a wolf population initially comprising 50 individuals is expected to persist only a few years longer, on average (71 years), than is a population initially comprising just a single reproductive pair (62 years). In contrast, substantially greater average prey abundance leads to dramatically longer expected persistence times. Autocorrelated prey dynamics result in a more complex distribution of extinction times than predicted by many extinction models. We contend that demographic stochasticity may pose the greatest threat to small, isolated wolf populations, although environmental stochasticity and genetic effects may compound this threat. Our work highlights the importance of considering social structure and resource dynamics in the development of population viability analyses.     

Investigations into a plankton population model: Mortality and its importance in climate change scenarios

The potential for marine plankton ecosystems to influence climate by the production of dimethylsulphide (DMS) has been an important topic of recent research into climate change. Several General Circulation Models, used to predict climate change, have or are being modified to include interactions of ecosystems with climate. Climate change necessitates that parameters within ecosystem models must change during long-term simulations, especially mortality parameters that increase as organisms are pushed toward the boundaries of their thermal tolerance. Changing mortality parameters can have profound influences on ecosystem model dynamics. There is therefore a pressing need to understand the influence of varying mortality parameters on the long-term behaviour of ecosystem models. This work examines the sensitivity of a model of DMS production by marine ecosystems to variations in three linear mortality coefficients. Significant differences in behaviour are observed, and we note the importance of these results in formulating ecosystem models for application in simulations of climate change.     

Integrating Landscape and Metapopulation Modeling Approaches: Viability of the Sharp-Tailed Grouse in a Dynamic Landscape

Abstract:  The lack of management experience at the landscape scale and the limited feasibility of experiments at this scale have increased the use of scenario modeling to analyze the effects of different management actions on focal species. However, current modeling approaches are poorly suited for the analysis of viability in dynamic landscapes. Demographic (e.g., metapopulation) models of species living in these landscapes do not incorporate the variability in spatial patterns of early successional habitats, and landscape models have not been linked to population viability models. We link a landscape model to a metapopulation model and demonstrate the use of this model by analyzing the effect of forest management options on the viability of the Sharp-tailed Grouse (  Tympanuchus phasianellus ) in the Pine Barrens region of northwestern Wisconsin (U.S.A.). This approach allows viability analysis based on landscape dynamics brought about by processes such as succession, disturbances, and silviculture. The landscape component of the model (LANDIS) predicts forest landscape dynamics in the form of a time series of raster maps. We combined these maps into a time series of patch structures, which formed the dynamic spatial structure of the metapopulation component (RAMAS). Our results showed that the viability of Sharp-tailed Grouse was sensitive to landscape dynamics and demographic variables such as fecundity and mortality. Ignoring the landscape dynamics gave overly optimistic results, and results based only on landscape dynamics (ignoring demography) lead to a different ranking of the management options than the ranking based on the more realistic model incorporating both landscape and demographic dynamics. Thus, models of species in dynamic landscapes must consider habitat and population dynamics simultaneously.     

The role of periodic boundary forcing in plankton pattern formation

We consider one and two-dimensional minimal models in plankton dynamics. The influence of oscillating boundary forcing functions as agents for triggering pattern formation is discussed. In particular it is found that in these conditions population waves arise for one dimensional models, while for two dimensional models, different amplitudes and frequencies in the boundary forcing generate definite patterns, mimicking the boundary term. This happens even though the model we investigate is very simple. The emergence of these features is an interesting metaphor for the fundamental biological problem of how pattern formation processes may be inevitable in natural heterogeneous ecosystems.     

Analysis of neighborhood dynamics of forest ecosystems using likelihood methods and modeling.

Advances in computing power in the past 20 years have led to a proliferation of spatially explicit, individual-based models of population and ecosystem dynamics. In forest ecosystems, the individual-based models encapsulate an emerging theory of "neighborhood" dynamics, in which fine-scale spatial interactions regulate the demography of component tree species. The spatial distribution of component species, in turn, regulates spatial variation in a whole host of community and ecosystem properties, with subsequent feedbacks on component species. The development of these models has been facilitated by development of new methods of analysis of field data, in which critical demographic rates and ecosystem processes are analyzed in terms of the spatial distributions of neighboring trees and physical environmental factors. The analyses are based on likelihood methods and information theory, and they allow a tight linkage between the models and explicit parameterization of the models from field data. Maximum likelihood methods have a long history of use for point and interval estimation in statistics. In contrast, likelihood principles have only more gradually emerged in ecology as the foundation for an alternative to traditional hypothesis testing. The alternative framework stresses the process of identifying and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. There are four general steps involved in a likelihood analysis: (1) model specification, (2) parameter estimation using maximum likelihood methods, (3) model comparison, and (4) model evaluation. Our goal in this paper is to review recent developments in the use of likelihood methods and modeling for the analysis of neighborhood processes in forest ecosystems. We will focus on a single class of processes, seed dispersal and seedling dispersion, because recent papers provide compelling evidence of the potential power of the approach, and illustrate some of the statistical challenges in applying the methods.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

From single fine roots to a black alder forest ecosystem: How system behaviour emerges from single component activities

Based on empirical findings in a natural black alder ecosystem in Northern Germany we developed an individual based model that integrates components of a black alder ecosystem interacting on different levels of organisation. The factors determining seasonal fine root biomass development of forest ecosystems are not yet fully understood.We used an object oriented model approach to investigate this complex matter for black alder trees. Processes like growth, storage, respiration, transport, nutrient mineralisation and uptake as well as interactions among these factors are described on the level of functionally differentiated plant organs (fine roots, coarse roots, stem, branches, leaves) and soil units. The object structure of the model is determined by spatial relations between plant modules as well as between plant modules and their local environment modules.As results of model application we found that (i) on the organ level, spatio-temporal plasticity of (root) growth allocation is related to spatio-temporal variation of resource availability, (ii) on the plant level, balanced root:shoot growth appears in response to variation of available resources light and nutrients, (iii) on the population level, tree stand development (population structure, self-thinning) resulted from coexistence and competition between plant individuals.For the understanding of the root compartment it seems relevant that the model implementation of local scale fine root dynamics is consistent with a self-organised large scale spatial heterogeneity of fine root activity pattern. On the other hand, fine-root dynamics cannot be explained as a result of autonomous dynamics. A reference to above-ground processes is a necessary condition and the overall plant seems to act as an integrator providing boundary conditions for local activity pattern. At the same time fine-root characteristics are of some importance for properties on hierarchically higher levels, e.g. co-existence in a tree population or element cycling in the ecosystem.As a conclusion, modelling of the spatio-temporal dynamics of tree root systems appears as a paradigmatic example of scale and organisation level integrating processes.     

Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models

Aquatic biogeochemical models are widely used as tools for understanding aquatic ecosystems and predicting their response to various stimuli (e.g., nutrient loading, toxic substances, climate change). Due to the complexity of these systems, such models are often elaborate and include a large number of estimated parameters. However, correspondingly large data sets are rarely available for calibration purposes, leading to models that may be overfit and possess reduced predictive capabilities. We apply, for the first time, information-theoretic model-selection techniques to a set of spatially explicit (1D) algal dynamics models of varying parameter dimension. We demonstrate that increases in complexity tend to produce a better model fit to calibration data, but beyond a certain degree of complexity the benefits of adding parameters are diminished (the risk of overfitting becomes greater). The particular approach taken here is computationally expensive, but several suggestions are made as to how multimodel methods may practically be extended to more sophisticated models.     

A methodological approach to develop “contaminant migration–population effects” models

The main aim of the present work is to discuss the methodological approaches that underpin the “contaminant migration–population effects” models for the evaluation of the detriment to populations of moving organisms in environmental systems with spatial and time dependent pollution levels. A technique to couple the equations controlling the population dynamics and the pollutant dispersion is described and discussed. The domain of application and the limitations of the methodology are analysed and illustrated by some examples. Possible alternative approaches are briefly presented.     

Metapopulation dynamics of a perennial plant, Succisa pratensis, in an agricultural landscape

Most metapopulation models neglect the local dynamics, and systems characterized by slow population turnover, time lags and non-equilibrium, are only rarely examined within a metapopulation context. In this study we used a realistic, spatially explicit, dynamic metapopulation model of a long-lived grassland plant, Succisa pratensis, to examine the relative importance of local population dynamics, and short and long-distance dispersal of seeds.     

An inverse ecosystem model of year-to-year variations with first order approximation to the annual mean fluxes

Ecosystems exhibit nonlinear dynamics that are often difficult to capture in models. Consequently, linearization is commonly applied to remove some of the uncertainties associated with the nonlinear terms. However, since the true model is unknown and the operating point to linearize the model about is uncertain, developing linear ecosystems models is non-trivial. To develop a linear ecosystem model, we assume that the annual mean state of an ecosystem is a minor bias from the long-term mean state. A first order approximation inverse model to govern the year-to-year dynamics of ecosystems whose characteristic time scales are less than 1 year is developed, through theoretically formulation, on the basis of steady state analysis, time scale separation and nondimensionalization. The approach is adept at predicting year-to-year variations and to tracking system response to changes in environmental drivers when compared to data generated with a standard nonlinear NPZD model.     

Population dynamics models based on cumulative density dependent feedback: A link to the logistic growth curve and a test for symmetry using aphid data

Density dependent feedback, based on cumulative population size, has been advocated to explain and mathematically characterize “boom and bust” population dynamics. Such feedback results in a bell-shaped population trajectory of the population density. Here, we note that this trajectory is mathematically described by the logistic probability density function. Consequently, the cumulative population follows a time trajectory that has the same shape as the cumulative logistic function. Thus, the Pearl–Verhulst logistic equation, widely used as a phenomenological model for density dependent population growth, can be interpreted as a model for cumulative rather than instantaneous population. We extend the cumulative density dependent differential equation model to allow skew in the bell-shaped population trajectory and present a simple statistical test for skewness. Model properties are exemplified by fitting population trajectories of the soybean aphid, Aphis glycines. The linkage between the mechanistic underpinnings of the logistic probability density function and cumulative distribution function models could open up new avenues for analyzing population data.     

Ecological Sustainability as a Conservation Concept

Neither the classic resource management concept of maximum sustainable yield nor the concept of sustainable development are useful to contemporary, nonanthropocentric, ecologically informed conservation biology. As an alternative, we advance an ecological definition of sustainability that is in better accord with biological conservation: meeting human needs without compromising the health of ecosystems. In addition to familiar benefit-cost constraints on human economic activity, we urge adding ecologic constraints. Projects are not choice-worthy if they compromise the health of the ecosystems in which human economic systems are embedded. Sustainability, so defined, is proffered as an approach to conservation that would complement wildlands preservation for ecological integrity, not substitute for wildlands preservation.     

Transient population dynamics in periodic matrix models: methodology and effects of cyclic permutations

Many biological populations are subject to periodically changing environments such as years with or without fire, or rotation of crop types. The dynamics and management options for such populations are frequently investigated using periodic matrix models. However the analysis is usually limited to long-term results (asymptotic population growth rate and its sensitivity to perturbations of vital rates). In non-periodic matrix models it has been shown that long-term results may be misleading as populations are rarely in their stable structure. We therefore develop methods to analyze transient dynamics of periodic matrix models. In particular, we show how to calculate the effects of perturbations on population size within and at the end of environmental cycles. Using a model of a weed population subject to a crop rotation, we show that different cyclic permutations produce different patterns of sensitivity of population size and different population sizes. By examining how the starting environment interacts with the initial conditions, we explain how different patterns arise. Such understanding is critical to developing effective management and monitoring strategies for populations subject to periodically recurring environments.