Diversity Waves in Collapse-Driven Population Dynamics

Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population, needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change incrementally due to stochastic births and deaths of individuals. Here we propose and model another redistribution mechanism driven by abrupt and severe reduction in size of the population of a single species freeing up resources for the remaining ones. This mechanism may be relevant e.g. for communities of bacteria, with strain-specific collapses caused e.g. by invading bacteriophages, or for other ecosystems where infectious diseases play an important role. The emergent dynamics of our system is characterized by cyclic ‘‘diversity waves’’ triggered by collapses of globally dominating populations. The population diversity peaks at the beginning of each wave and exponentially decreases afterwards. Species abundances have bimodal time-aggregated distribution with the lower peak formed by populations of recently collapsed or newly introduced species while the upper peak - species that has not yet collapsed in the current wave. In most waves both upper and lower peaks are composed of several smaller peaks. This self-organized hierarchical peak structure has a long-term memory transmitted across several waves. It gives rise to a scale-free tail of the time-aggregated population distribution with a universal exponent of 1.7. We show that diversity wave dynamics is robust with respect to variations in the rules of our model such as diffusion between multiple environments, species-specific growth and extinction rates, and bet-hedging strategies.     

Food webs in space: On the interplay of dynamic instability and spatial processes

Ecologists increasingly recognize that a consideration of spatial dynamics is essential for resolving many classical problems in community ecology. In the present paper, I argue that understanding how trophic interactions influence population stability can have important implications for the expression of spatial processes. I use two examples to illustrate this point. The first example has to do with spatial determinants of food chain length. Prior theoretical and empirical work has suggested that colonization–extinction dynamics can influence food chain length, at least for specialist consumers. I briefly review evidence and prior theory that food chain length is sensitive to area. A metacommunity scenario, in which each of various patches can have a food chain varying in length (but in which a consumer is not present on a patch unless its required resource is also present), shows that alternative landscape states are possible. This possibility arises if top predators moderate unstable interactions between intermediate predators and basal resources. The second example has to do with the impact of recurrent immigration on the stability of persistent populations. Immigration can either stabilize or destabilize local population dynamics. Moreover, an increase in immigration can decrease average population size for unstable populations with direct density-dependence, or in predator–prey systems with saturating functional responses. These theoretical models suggest that the interplay of temporal variation and spatial fluxes can lead to novel qualitative phenomena.     

Phylodynamic Inference for Structured Epidemiological Models

Coalescent theory is routinely used to estimate past population dynamics and demographic parameters from genealogies. While early work in coalescent theory only considered simple demographic models, advances in theory have allowed for increasingly complex demographic scenarios to be considered. The success of this approach has lead to coalescent-based inference methods being applied to populations with rapidly changing population dynamics, including pathogens like RNA viruses. However, fitting epidemiological models to genealogies via coalescent models remains a challenging task, because pathogen populations often exhibit complex, nonlinear dynamics and are structured by multiple factors. Moreover, it often becomes necessary to consider stochastic variation in population dynamics when fitting such complex models to real data. Using recently developed structured coalescent models that accommodate complex population dynamics and population structure, we develop a statistical framework for fitting stochastic epidemiological models to genealogies. By combining particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a wide class of stochastic, nonlinear epidemiological models with different forms of population structure to genealogies. We demonstrate our framework using two structured epidemiological models: a model with disease progression between multiple stages of infection and a two-population model reflecting spatial structure. We apply the multi-stage model to HIV genealogies and show that the proposed method can be used to estimate the stage-specific transmission rates and prevalence of HIV. Finally, using the two-population model we explore how much information about population structure is contained in genealogies and what sample sizes are necessary to reliably infer parameters like migration rates.     

Evidence of peak-to-peak dynamics in ecology

Remarkably irregular peaks characterize the dynamics of many plant and animal populations. As such peaks are often associated with undesirable consequences (e.g. pest outbreaks, epidemics, forest fires), the forecast of the forthcoming peak is a problem of major concern. Here we show, through the analysis of a number of models and of some of the longest and most celebrated ecological time-series, that the intensity of the forthcoming peak can often be predicted simply from the previous peaks. When this is possible, one can also predict the time of occurrence of the forthcoming peak.     

A ratio-dependent food chain model and its applications to biological control

While biological controls have been successfully and frequently implemented by nature and human, plausible mathematical models are yet to be found to explain the often observed deterministic extinctions of both pest and control agent in such processes. In this paper we study a three trophic level food chain model with ratio-dependent Michaelis-Menten type functional responses. We shall show that this model is rich in boundary dynamics and is capable of generating such extinction dynamics. Two trophic level Michaelis-Menten type ratio-dependent predator-prey system was globally and systematically analyzed in details recently. A distinct and realistic feature of ratio-dependence is its capability of producing the extinction of prey species, and hence the collapse of the system. Another distinctive feature of this model is that its dynamical outcomes may depend on initial populations levels. Theses features, if preserved in a three trophic food chain model, make it appealing for modelling certain biological control processes (where prey is a plant species, middle predator as a pest, and top predator as a biological control agent) where the simultaneous extinctions of pest and control agent is the hallmark of their successes and are usually dependent on the amount of control agent. Our results indicate that this extinction dynamics and sensitivity to initial population levels are not only preserved, but also enriched in the three trophic level food chain model. Specifically, we provide partial answers to questions such as: under what scenarios a potential biological control may be successful, and when it may fail. We also study the questions such as what conditions ensure the coexistence of all the three species in the forms of a stable steady state and limit cycle, respectively. A multiple attractor scenario is found.     

Chaos and Peak-to-Peak Dynamics in a Plankton–Fish Model

A plankton–fish model, comprising phosphorus, algae, zooplankton, and young fish, with light intensity and water temperature varying periodically with the seasons, is analyzed in this paper. For realistic values of the parameters the model behaves chaotically, but its dynamics within the strange attractor can be described by a few one-dimensional maps that allow one to forecast the next yearly peak of plankton or fish from the last peaks. This property is an unambiguous mark of a special form of chaos. Unfortunately, the estimate of such peak-to-peak maps from field data is possible only if plankton or young fish biomass has been sampled accurately and frequently for a paramount number of years. In conclusion, the analysis shows that it might be that plankton dynamics are characterized by an interesting and peculiar form of chaos, but that inferences from recorded data on the existence of these forms of chaos are premature.     

The Effects of Spatial Scale on Trophic Interactions

Food chain models have dominated empirical studies of trophic interactions in the past decades, and have lead to important insights into the factors that control ecological communities. Despite the importance of food chain models in instigating ecological investigations, many empirical studies still show a strong deviation from the dynamics that food chain models predict. We present a theoretical framework that explains some of the discrepancies by showing that trophic interactions are likely to be strongly influenced by the spatial configuration of consumers and their resources. Differences in the spatial scale at which consumers and their resources function lead to uncoupling of the population dynamics of the interacting species, and may explain overexploitation and depletion of resource populations. We discuss how changed land use, likely the most prominent future stress on natural systems, may affect food web dynamics by interfering with the scale of interaction between consumers and their resource.     

Inference for nonlinear epidemiological models using genealogies and time series

Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.     

Food webs: a plea for parasites

Parasites have the capacity to regulate host populations and may be important determinants of community structure, yet they are usually neglected in studies of food webs. Parasites can provide much of the information on host biology, such as diet and migration, that is necessary to construct accurate webs. Because many parasites have complex life cycles that involve several different hosts, and often depend on trophic interactions for transmission, parasites provide complementary views of web structure and dynamics. Incorporation of parasites in food webs can substantially after baste web properties, Including connectance, chain length and proportions of top and basal species, and can allow the testing of specific hypotheses related to food-web dynamics.     

Mechanisms of coexistence in diverse herbivore–carnivore assemblages: demographic,temporal and spatial heterogeneities affecting prey vulnerability

Simple models coupling the dynamics of single predators to single prey populations tend to generate oscillatory dynamics of both predator and prey, or extirpation of the prey followed by that of the predator. In reality, such oscillatory dynamics may be counteracted by prey refugia or by opportunities for prey switching by the predator in multi‐prey assemblages. How these mechanisms operate depends on relative prey vulnerability, a factor ignored in simple interactive models. I outline how compositional, temporal, demographic and spatial heterogeneities help explain the contrasting effects of top predators on large herbivore abundance and population dynamics in species‐rich African savanna ecosystems compared with less species‐diverse northern temperate or subarctic ecosystems. Demographically, mortality inflicted by predation depends on the relative size and life history stage of the prey. Because all animals eventually die and are consumed by various carnivores, the additive component of the mortality inflicted is somewhat less than the predation rate. Prey vulnerability varies annually and seasonally, and between day and night. Spatial variation in the risk of predation depends on vegetation cover as well as on the availability of food resources. During times of food shortage, herbivores become prompted to occupy more risky habitats retaining more food. Predator concentrations dependent on the abundance of primary prey species may restrict the occurrence of other potential prey species less resistant to predation. The presence of multiple herbivore species of similar size in African savannas allows the top predator, the lion, to shift its prey selection flexibly dependent on changing prey vulnerability. Hence top–down and bottom–up influences on herbivore populations are intrinsically entangled. Models coupling the population dynamics of predators and prey need to accommodate the changing influences of prey demography, temporal variation in environmental conditions, and spatial variation in the relative vulnerability of alternative prey species to predation. Synthesis While re‐established predators have had major impacts on prey populations in northern temperate regions, multiple large herbivore species typically coexist along with diverse carnivores in African savanna ecosystems. In order to explain these contrasting outcomes, certain functional heterogeneities must be recognised, including relative vulnerability of alternative prey, temporal variation in the risk of predation, demographic differences in susceptibility to predation, and spatial contrasts in exposure to predation. Food shortfalls prompt herbivores to exploit more risky habitats, meaning that top–down and bottom–up influences on prey populations are intrinsically entangled. Models coupling the interactive dynamics of predator and prey populations need to incorporate these varying influences on relative prey vulnerability.     

Study of a tri-trophic prey-dependent food chain model of interacting populations

The current paper accounts for the influence of intra-specific competition among predators in a prey dependent tri-trophic food chain model of interacting populations. We offer a detailed mathematical analysis of the proposed food chain model to illustrate some of the significant results that has arisen from the interplay of deterministic ecological phenomena and processes. Biologically feasible equilibria of the system are observed and the behaviours of the system around each of them are described. In particular, persistence, stability (local and global) and bifurcation (saddle-node, transcritical, Hopf–Andronov) analysis of this model are obtained. Relevant results from previous well known food chain models are compared with the current findings. Global stability analysis is also carried out by constructing appropriate Lyapunov functions. Numerical simulations show that the present system is capable enough to produce chaotic dynamics when the rate of self-interaction is very low. On the other hand such chaotic behaviour disappears for a certain value of the rate of self interaction. In addition, numerical simulations with experimented parameters values confirm the analytical results and shows that intra-specific competitions bears a potential role in controlling the chaotic dynamics of the system; and thus the role of self interactions in food chain model is illustrated first time. Finally, a discussion of the ecological applications of the analytical and numerical findings concludes the paper.     

Trophic exploitation in grassland food chains: simple models and a field experiment

This study provides insight into the importance of top carnivores (top-down control) and nutrient inputs (bottom-up control) in structuring food chains in a terrestrial grassland system. Qualitative predictions about food chain structure are generated using 4 simple models, each differing in assumptions about some key component in the population dynamics of the herbivore trophic level. The four model systems can be classified broadly into two groups (1) those that assume plant resource intake by herbivores is limited by search rate and handling time as described by classic Lotka-Volterra models; and (2) those that assume plant resource intake by herbivores is limited externally by the supply rate of resources as described by alternatives to Lotka-Volterra formulations. The first class of models tends to ascribe greater importance to top-down control of food chain structure whereas the second class places greater weight on bottom-up control. I evaluated the model predictions using experimentally assembled grassland food chains in which I manipulated nutrient inputs and carnivore (wolf spider) abundance to determine the degree of top-down and bottom-up control of grassland plants and herbivores (grasshoppers). The experimental results were most consistent with predictions of the second class of models implying a predominance of bottom-up control of food chain structure.     

Ecological consequences of global bifurcations in some food chain models

Food chain models of ordinary differential equations (ode’s) are often used in ecology to gain insight in the dynamics of populations of species, and the interactions of these species with each other and their environment. One powerful analysis technique is bifurcation analysis, focusing on the changes in long-term (asymptotic) behaviour under parameter variation. For the detection of local bifurcations there exists standardised software, but until quite recently most software did not include any capabilities for the detection and continuation of global bifurcations. We focus here on the occurrence of global bifurcations in four food chain models, and discuss the implications of their occurrence. In two stoichiometric models (one piecewise continuous, one smooth) there exists a homoclinic bifurcation, that results in the disappearance of a limit cycle attractor. Instead, a stable positive equilibrium becomes the global attractor. The models are also capable of bistability. In two three-dimensional models a Shil’nikov homoclinic bifurcation functions as the organising centre of chaos, while tangencies of homoclinic cycle-to-cycle connections ‘cut’ the chaotic attractors, which is associated with boundary crises. In one model this leads to extinction of the top predator, while in the other model hysteresis occurs. The types of ecological events occurring because of a global bifurcation will be categorized. Global bifurcations are always catastrophic, leading to the disappearance or merging of attractors. However, there is no 1-on-1 coupling between global bifurcation type and the possible ecological consequences. This only emphasizes the importance of including global bifurcations in the analysis of food chain models.     

Generation cycles in stage structured populations

Some insect populations exhibit cycles in which successive population peaks may correspond to effectively discrete generations. Motivated by this observation, we investigate the structure of matriarchal generations in five simple, continuous-time, stage structure models in order to determine the proportion of individuals in one population peak who are the offspring of individuals in the pervious peak. We conclude that in certain models (including a model of Nicholson's blowflies) successive population peaks do not correspond to discrete generations, whereas in others (including some models of uniform larval competition) successive peaks may well approximate discrete generations. In all models, however, there is eventually significant overlap of generations.     

Effect of parity on productivity and sustainability of Lotka–Volterra food chains

Hairston, Slobodkin, and Smith conjectured that top down forces act on food chains, which opposed the previously accepted theory that bottom up forces exclusively dictate the dynamics of populations. We model food chains using the Lotka–Volterra predation model and derive sustainability constants which determine which species will persist or go extinct. Further, we show that the productivity of a sustainable food chain with even trophic levels is predator regulated, or top down, while a sustainable food chain with odd trophic levels is resource limited, which is bottom up, which is consistent with current ecological theory.     

Biological filtering of correlated environments: towards a generalised Moran theorem

Many species from diverse taxa are known to display synchronous fluctuations across vast geographical ranges. It is often thought that climate factors influencing the growth of conspecific populations are correlated over large distances and hence produce the synchronous population dynamics – an effect known as the Moran effect. However, for species embedded in a food web the Moran effect needs not necessarily influence the focal species directly, but can act indirectly through other species. Such an indirect synchronization can also occur in an age-structured population, where the correlated environment of one age-class causes synchronous fluctuations of another. Here, we investigate this indirect Moran effect. We find first of all that synchrony is readily transferred through food webs or between age classes, which complicates the identification of the underlying synchronizing factor. Secondly, we find puzzling cases, where synchrony is enhanced as it is filtered through a food web or between age-classes. Our results also apply to systems of different species, but with closely matching dynamics.     

Bi-trophic food chain dynamics with multiple component populations

Food web models describe the patterns of material and energy flow in communities. In classical food web models the state of each population is described by a single variable which represents, for instance, the biomass or the number of individuals that make up the population. However, in a number of models proposed recently in the literature the individual organisms consist of two components. In addition to the structural component there is an internal pool of nutrients, lipids or reserves. Consequently the population model for each trophic level is described by two state variables instead of one. As a result the classical predator-prey interaction formalisms have to be revised. In our model time budgets with actions as searching and handling provide the formulation of the functional response for both components. In the model, assimilation of the ingested two prey components is done in parallel and the extracted energy is added to a predators reserve pool. The reserves are used for vital processes; growth, reproduction and maintenance. We will explore the top-down modelling approach where the perspective is from the community. We will demonstrate that this approach facilitates a check on the balance equations for mass and energy at this level of organization. Here it will be shown that, if the individual is allowed to shrink when the energy reserves are in short to pay the maintenance costs, the growth process has to be 100% effective. This is unrealistic and some alternative model formulations are discussed. The long-term dynamics of a microbial food chain in the chemostat are studied using bifurcation analysis. The dilution rate and the concentration of nutrients in the reservoir are the bifurcation parameters. The studied microbial bi-trophic food chain with two-component populations shows chaotic behaviour.     

Simultaneous effects of food limitation and inducible resistance on herbivore population dynamics

Many herbivore populations fluctuate temporally, but the causes of those fluctuations remain unclear. Plant inducible resistance can theoretically cause herbivore population fluctuations, because herbivory may induce plant changes that reduce the survival or reproduction of later-feeding herbivores. Herbivory can also simply reduce the quantity of food available for later feeders and this, too, can cause population fluctuations. Inducible resistance and food limitation often occur simultaneously, yet whether they jointly facilitate or suppress herbivore fluctuations remains largely unexplored. We present models that suggest that food limitation and inducible resistance may have synergistic effects on herbivore population dynamics. The population-level response of the food plant to herbivory and the details of how inducible resistance affects herbivore performance both influence the resulting herbivore dynamics. Our results identify some biological properties of plant-herbivore systems that might determine whether or not cycles occur, and suggest that future empirical and theoretical population dynamics studies should account for the effects of both food limitation and inducible resistance.     

EVOLUTION OF GENERALISTS AND SPECIALISTS IN SPATIALLY HETEROGENEOUS ENVIRONMENTS

Quantitative genetic models are used to investigate the evolution of generalists and specialists in a coarse-grained environment with two habitat types when there are costs attached to being a generalist. The outcomes for soft and hard selection models are qualitatively different. Under soft selection (e.g., for juvenile or male-reproductive traits) the population evolves towards the single peak in the adaptive landscape. At equilibrium, the population mean phenotype is a compromise between the reaction that would be optimal in both habitats and the reaction with the lowest cost. Furthermore, the equilibrium is closer to the optimal phenotype in the most frequent habitat, or the habitat in which selection on the focal trait is stronger. A specialist genotype always has a lower fitness than a generalist, even when the costs are high. In contrast, under hard selection (e.g., for adult or female-reproductive traits) the adaptive landscape can have one, two, or three peaks; a peak represents a population specialized to one habitat, equally adapted to both habitats, or an intermediate. One peak is always found when the reaction with the lowest cost is not much different from the optimal reaction, and this situation is similar to the soft selection case. However, multiple peaks are present when the costs become higher, and the course of evolution is then determined by initial conditions, and the region of attraction of each peak. This implies that the evolution of specialization and phenotypic plasticity may not only depend on selection regimes within habitats, but also on contingent, historical events (migration, mutation). Furthermore, the evolutionary dynamics in changing environments can be widely different for populations under hard and soft selection. Approaches to measure costs in natural and experimental populations are discussed.