The Influence Of Demographic Stochasticity On Population Dynamics

The dynamics of population systems cannot be understood within the framework of ordinary differential equations, which assume that the number of interacting agents is infinite. With recent advances in ecology, biochemistry and genetics it is becoming increasingly clear that real systems are in fact subject to a great deal of noise. Relevant examples include social insects competing for resources, molecules undergoing chemical reactions in a cell and a pool of genomes subject to evolution. When the population size is small, novel macroscopic phenomena can arise, which can be analyzed using the theory of stochastic processes. This thesis is centered on two unsolved problems in population dynamics: the symmetry breaking observed in foraging populations and the robustness of spatial patterns. We argue that these problems can be resolved with the help of two novel concepts: noise-induced bistable states and stochastic patterns.

1. Demographic and environmental stochasticity -- 2. Extinction dynamics -- 3. Age structure -- 4. Spatial structure -- 5. Population viability analysis -- 6. Sustainable harvesting -- 7. Species diversity -- 8. Community dynamics.

Populations living in natural environments experience fluctuations in environmental conditions that drive variability in demographic rates. This dissertation develops new and existing mathematical methods for studying environmental stochasticity and uses these tools to investigate the role of environmental stochasticity in driving observed population dynamics and plant life history evolution. In the first two chapters I develop new approaches to a classic method in population biology, the life table response experiment (LTRE). Whereas existing methods used time-averaged demographic rates and deterministic sensitivities to decompose observed differences in population growth rates, this new method allows estimation of the contributions to those differences made by variances in demographic rates as well as by mean rate values. I use this stochastic LTRE to show how differential variability in the vital rates of Anthyllis vulneraria (kidney vetch) contribute to differences in the population growth rates of nine populations growing in southwest Belgium; we also show how the effects of demographic rate variability depend on soil depth, where the greater moisture retention of deeper soils buffers populations against the otherwise negative effects of demographic variability. The second chapter provides a different approach to LTRE that uses an iterated two-factor decomposition of the small noise approximation of the stochastic population growth rate to quantify contributions to that growth rate made by: (i) mean vital rates, (ii) temporal variability in vital rates, (iii) elasticities of the population growth rate to individual vital rates, and (iv) correlations between vital rates across the study period. Contributions of elasticities tell us about differences in local selection pressures acting on distinct populations and contributions of correlations tell us about differences in the phenotypic tradeoffs associated with vital rates. I use this new method to show how these differences drive dynamics in two species: Anthyllis vulneraria (the same populations studied in the first chapter) and Cypripedium calceolus (lady's slipper orchid). In Anthyllis vulneraria, variability in large adult fertility and seedling survival made the largest contributions; there were also effects of differences in elasticities of large adult fertility and survival, as well as differences in the correlations between rapid growth and survival in seedlings (a survival cost of rapid early development), between large adult fertility and survival (a survival cost of reproduction) and between large adult fertility and seedling survival. In Cypripedium calceolus, population growth rates were driven most by differences in the elasticities to the probabilities of adult stasis vs. entering dormancy, as well as by differences in the variability and tradeoffs associated with adult dormancy; correlation played a role through differences in the survival payoff of dormancy vs. the complimentary fertility cost of dormancy in terms of lost opportunity for reproduction. The third and final chapter investigates the role of fire disturbance in driving the life histories and population-level dynamics of five woody plant species growing in the Brazilian cerrado, a savannah-forest mosaic in which woody vegetation cover is primarily mediated by fire disturbance. This study presents a set of diagnostics that use demographic responses to recurring disturbance to categorize species along a continuum of adaptation: on one end we find 'resistant' species that must weather disturbance in order to attain large sizes that are buffered against fire-induced mortality; on the other end we find 'resilient' species that are relatively indifferent to disturbance and harness transient opportunities afforded by early post-fire successional habitats in order to take advantage of increased nutrient availability and reduced competition. Each of these chapters uses stochastic demographic analysis to extend theory describing the dynamics of populations in variable environments; together, these studies present a variegated perspective on the role of environmental stochasticity that provides new methods and novel perspectives that should be useful in the study of population biology and life history evolution.

In this contribution, several probabilistic tools to study population dynamics are developed. The focus is on scaling limits of qualitatively different stochastic individual based models and the long time behavior of some classes of limiting processes. Structured population dynamics are modeled by measure-valued processes describing the individual behaviors and taking into account the demographic and mutational parameters, and possible interactions between individuals. Many quantitative parameters appear in these models and several relevant normalizations are considered, leading to infinite-dimensional deterministic or stochastic large-population approximations. Biologically relevant questions are considered, such as extinction criteria, the effect of large birth events, the impact of environmental catastrophes, the mutation-selection trade-off, recovery criteria in parasite infections, genealogical properties of a sample of individuals. These notes originated from a lecture series on Structured Population Dynamics at Ecole polytechnique (France). Vincent Bansaye and Sylvie Méléard are Professors at Ecole Polytechnique (France). They are a specialists of branching processes and random particle systems in biology. Most of their research concerns the applications of probability to biodiversity, ecology and evolution.

This book demonstrates that population structure and dynamics can be reconstructed by stochastic analysis. Population projection is usually based on age-structured population models. These models consist of age-dependent fertility and mortality, whereas birth and death processes generally arise from states of individuals. For example, a number of seeds are proportional to tree size, and amount of income and savings are the basis of decision making for birth behavior in human beings. Thus, even though individuals belong to an identical cohort, they have different fertility and mortality. To treat this kind of individual heterogeneity, stochastic state transitions are reasonable rather than the deterministic states. This book extends deterministic systems to stochastic systems specifically, constructing a state transition model represented by stochastic differential equations. The diffusion process generated by stochastic differential equations provides statistics determining population dynamics, i.e., heterogeneity is incorporated in population dynamics as its statistics. Applying this perspective to demography and evolutionary biology, we can consider the role of heterogeneity in life history or evolution. These concepts are provided to readers with explanations of stochastic analysis.

Here, biologists and statisticians come together in an interdisciplinary synthesis with the aim of developing new methods to overcome the most significant challenges and constraints faced by quantitative biologists seeking to model demographic rates.

Population Dynamics covers the proceedings of a symposium conducted by the Mathematics Research Center, The University of Wisconsin, Madison on June 19-21, 1972. The book focuses on the application of mathematics to the study of human population growth. The selection first offers information on population waves and the properties of a stochastic attraction model. Discussions focus on social distance, limiting behavior of the model, mathematical development, population increase and retirement pensions, natural periodicity in the demographic system, trends in generational stability, mobility in unstable populations, and the Easterlin effect. The text then takes a look at the sampling frame as a determinant of observed distributions of duration variables and comparison of alternative marriage models, including plausible marriage models, axioms for marriage functions, birth intervals, and computer simulation of prospective and interior birth interval lengths. The manuscript ponders on contraceptive impact over several generations, estimation of the risk of conception from censored data, and influence of cause of death structure on age-patterns of mortality. Topics include distributions of conception times, simulation of experiments, potential fertility of users, and length of protection. The selection is a valuable reference for researchers interested in population dynamics.

This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations. The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity, population (within a metapopulation), or species (for multi-species models). The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models. The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.