Dynamics Of Single Species Research Articles

The existence and strength of higher order interactions is sensitive to environmental context.

One strategy for understanding the dynamics of any complex system, such as a community of competing species, is to study the dynamics of parts of the system in isolation. Ecological communities can be decomposed into single species, and pairs of interacting species. This reductionist strategy assumes that whole-community dynamics are predictable and explainable from knowledge of the dynamics of single species and pairs of species. This assumption will be violated if higher order interactions (HOIs) are strong. Theory predicts that HOIs should be common. But it is difficult to detect HOIs, and to infer their long-term consequences for species coexistence, solely from short-term data. I conducted a protist microcosm experiment to test for HOIs among competing bacterivorous ciliates, and test the sensitivity of HOIs to environmental context. I grew three competing ciliate species in all possible combinations at each of two resource enrichment levels, and used the population dynamic data from the one- and two-species treatments to parameterize a competition model at each enrichment level. I then compared the predictions of the parameterized model to the dynamics of the whole community (three-species treatment). I found that the existence, and thus strength, of HOIs was environment dependent. I found a strong HOI at low enrichment, which enabled the persistence of a species that would otherwise have been competitively excluded. At high enrichment, three-species dynamics could be predicted from a parameterized model of one- and two-species dynamics, provided that the model accounted for nonlinear intraspecific density dependence. The results provide one of the first rigorous demonstrations of the long-term consequences of HOIs for species coexistence, and demonstrate the context dependence of HOIs. HOIs create difficult challenges for predicting and explaining species coexistence in nature.

The adventive species community changes in the Cheboksary reservoir during 2001-2021

Generally publications devoted to invasive fish species in the Volga-Kama cascade of reservoirs represents relative indicators and do not provide data on the abundance, numbers and invasive communities’ single species dynamics. It does not allow providing a correct assessment of population’s state and changes in the nonindigenous species composition. In this work, changes in the abundance and species structure of the Cheboksary reservoir’s invasive fish community during 2001-2021 were estimated. The total fish count was determined on data of active fishing gears catches and precise reservoir area. Differentiation into mass, widespread and relatively numerous and locally found with a small average abundance species was noted. A decrease in the species evenness of the invasive community was recorded in consequence of the Black Sea-Caspian sprat Clupeonella cultriventris (Nordmann, 1840) and the round goby Neogobius melanostomus (Pallas, 1814) domination rise. The Black Sea-Caspian sprat and the round goby put together for 98.7% of the total number of invasive fish in the current period. Due to this two dominant species there is a tendency to increase the relative abundance of invasive fish species in the reservoir up to 561 unit/ha despite a significant decrease in the abundance of some other invaders.

Effects of density, species interactions, and environmental stochasticity on the dynamics of British bird communities

Our knowledge of the factors affecting species abundances is mainly based on time‐series analyses of a few well‐studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a joint species distribution model to long‐term time‐series data on British bird communities to examine the relative contribution of intra‐ and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatiotemporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single‐species dynamics are also strongly affecting dynamics of communities in time and space.

Critical patch size reduction by heterogeneous diffusion.

Population survival depends on a large set of factors and on how they are distributed in space. Due to landscape heterogeneity, species can occupy particular regions that provide the ideal scenario for development, working as a refuge from harmful environmental conditions. Survival occurs if population growth overcomes the losses caused by adventurous individuals that cross the patch edge. In this work, we consider a single species dynamics in a patch with a space-dependent diffusion coefficient. We show analytically, within the Stratonovich framework, that heterogeneous diffusion reduces the minimal patch size for population survival when contrasted with the homogeneous case with the same average diffusivity. Furthermore, this result is robust regardless of the particular choice of the diffusion coefficient profile. We also discuss how this picture changes beyond the Stratonovich framework. Particularly, the Itô case, which is nonanticipative, can promote the opposite effect, while Hänggi-Klimontovich interpretation reinforces the reduction effect.

Delay-induced instabilities of stationary solutions in a single species nonlocal hyperbolic-parabolic population model

The present work investigates the effects of maturation and dispersal delays on dynamics of single species populations. Both delays have been incorporated in a single species nonlocal hyperbolic-parabolic population model, which admits traveling and stationary wave solutions. We reduce the model into various forms and obtain the corresponding analytical solutions. Analysis of the reduced models indicates that the dispersal delay can result in loss of monotonicity, where the solutions oscillate as they converge to a positive equilibrium. The stability analysis of the general model reveals that the maturation time delay admits a Hopf bifurcation threshold, which is expressed as a function of the dispersal delay. The numerical simulations of the general model suggest that the global stability of the stationary wave solutions is lost when the dispersal delay is increased from zero. In conclusion, population models with maturation and dispersal delays can give new insights into the complex dynamics of single species.

Dynamical bunching and density peaks in expanding Coulomb clouds

Expansion dynamics of single-species, non-neutral clouds, such as electron bunches used in ultrafast electron microscopy, show novel behavior due to high acceleration of particles in the cloud interior. This often leads to electron bunching and dynamical formation of a density shock in the outer regions of the bunch. We develop analytic fluid models to capture these effects, and the analytic predictions are validated by PIC and N-particle simulations. In the space-charge dominated regime, two and three dimensional systems with Gaussian initial densities show bunching and a strong shock response, while one dimensional systems do not; moreover these effects can be tuned using the initial particle density profile and velocity chirp.

Effects of an increased temperature regime on the population dynamics and species interactions of marine nematodes

Although changes in average environmental conditions can have serious consequences, the main impacts of global climate change on populations and communities may well result from changes in short-term climate variability. Both an increased frequency and intensity of extremes and changing amplitudes of diurnal temperature fluctuations may affect the fitness of species and the interactions between them. Such changing temperature regimes may affect reproductive success, population dynamics, species interactions and community structure. The present study compares the effects of an increased temperature regime with diurnal fluctuations with those of a constant temperature regime on the fitness and population dynamics of free-living marine nematodes and on their interspecific interactions. Microcosm experiments were performed on two congeneric monhysterid nematode species, which co-occur in their natural habitat, under a constant vs. an increased fluctuating temperature regime. The latter affected population dynamics of single species and altered the outcome of the interspecific interaction from a symmetrical to an asymmetrical inhibitory effect of D. meyli over D. oschei. Changes in the amplitude of diurnal temperature fluctuations as well as in the frequency of extreme temperatures may be very important determinants of the effects of temperature change on species interactions, potentially affecting assemblage structure and ecosystem functioning.

Global Asymptotic Stability for Discrete Single Species Population Models

We present some basic discrete models in populations dynamics of single species with several age classes. Starting with the basic Beverton-Holt model that describes the change of single species we discuss its basic properties such as a convergence of all solutions to the equilibrium, oscillation of solutions about the equilibrium solutions, Allee’s effect, and Jillson’s effect. We consider the effect of the constant and periodic immigration and emigration on the global properties of Beverton-Holt model. We also consider the effect of the periodic environment on the global properties of Beverton-Holt model.

Single species dynamics under climate change

We propose a general mathematical model describing the growth and dispersal of a single species living in a 1-D spatially discrete array of habitat patches affected by a sustained and directional change in climate. Our model accounts for two important characteristics of the climate change phenomenon: (1) Scale dependency: different species may perceive the change in the environment as occurring at different rates because they perceive the environment at different scales, and (2) measure dependency: different species measure the environment differently in the sense that they may be sensible to or cue in on different aspects of it (e.g., maximum temperature, minimum temperature, accumulated temperature) which is associated with their physiological, ecological, and life history attributes, which renders some characteristics of the environment more biologically relevant than others. We show that the deterioration in the quality of habitable patches as a consequence of climate change drives the species to extinction when dispersal is not possible; otherwise, we proof and provide a numerical example that, depending on the velocity of climate change, the scale at which a species measures it, and the particular attribute of the environment that is more biologically relevant to the species under analysis, there is always a migration strategy that allows the persistence of the species such that it tracks its niche conditions through space, thus shifting its geographic range. Our mathematical analysis provides a general framework to analyze species’ responses to climate change as a relational property of a given species in interaction with a change in climate. In particular, we can analyze the persistence of species by taking into account the ways in which they measure and filter the environment. Indeed, one of our main conclusions is that there is not a single climate change but many, as it depends on the interaction between a particular species and climate. Thus, the problem is more complex than assumed by analytically tractable models of species responses to climate change.

Neutral or non‐neutral communities: temporal dynamics provide the answer

Neutral models for community dynamics are based on the assumption that temporal variation is only influenced by demographic stochasticity, ignoring the influence of fluctuations in the environment. Demographic and environmental stochasticity are known to have very different effects on the population dynamics of single species. Communities, however, are usually analyzed with emphasis on the shape of the species abundance distribution rather than on their dynamical characteristics. Accordingly, examination of the validity of the neutral model has typically been based on statistical analyses of the shape of the species abundance distribution or form of the species–area relationships. Here we illustrate that both demographic and environmental noise must be included to obtain realistic description of community dynamics. We do this by a simple approach comparing two different dynamic models both leading to Fisher's log series species abundance distribution: the neutral model with only demographic noise, and a limiting form of an approximate gamma model most strongly affected by environmental noise. While keeping Fisher's diversity parameter α constant, the turnover rates for observable species become much greater and average life‐times of species much smaller in the gamma model than in the neutral model. In practice, the neutral model tends to produce unrealistically small turnover rates for large populations. This illustrates that realistic assumptions about the stochastic components of the underlying population dynamics of single species are important for correctly characterizing dynamical changes in species composition of communities. This approach can provide a powerful tool for identifying the principal factors affecting the structure of communities.

A Multiscale Approach to Plant Disease Using the Metacommunity Concept.

Plant disease arises from the interaction of processes occurring at multiple spatial and temporal scales. With new tools such as next-generation sequencing, we are learning about the diversity of microbes circulating within and among plant populations and often coinhabiting host individuals. The proliferation of pathogenic microbes depends on single-species dynamics and multispecies interactions occurring within and among host cells, the spatial organization and genetic landscape of hosts, the frequency and mode of transmission among hosts and host populations, and the abiotic environmental context. Here, we examine empirical evidence from these multiple scales to assess the utility of metacommunity theory, a theoretical framework developed for free-living organisms to further our understanding of and assist in predicting plant-pathogen infection and spread. We suggest that deeper understanding of disease dynamics can arise through the application of this conceptual framework at scales ranging from individual cells to landscapes. In addition, we use this multiscale theoretical perspective to synthesize existing knowledge, generate novel hypotheses, and point toward promising future opportunities for the study of plant pathogens in natural populations.

Avoiding tipping points in fisheries management through Gaussian process dynamic programming.

Model uncertainty and limited data are fundamental challenges to robust management of human intervention in a natural system. These challenges are acutely highlighted by concerns that many ecological systems may contain tipping points, such as Allee population sizes. Before a collapse, we do not know where the tipping points lie, if they exist at all. Hence, we know neither a complete model of the system dynamics nor do we have access to data in some large region of state space where such a tipping point might exist. We illustrate how a Bayesian non-parametric approach using a Gaussian process (GP) prior provides a flexible representation of this inherent uncertainty. We embed GPs in a stochastic dynamic programming framework in order to make robust management predictions with both model uncertainty and limited data. We use simulations to evaluate this approach as compared with the standard approach of using model selection to choose from a set of candidate models. We find that model selection erroneously favours models without tipping points, leading to harvest policies that guarantee extinction. The Gaussian process dynamic programming (GPDP) performs nearly as well as the true model and significantly outperforms standard approaches. We illustrate this using examples of simulated single-species dynamics, where the standard model selection approach should be most effective and find that it still fails to account for uncertainty appropriately and leads to population crashes, while management based on the GPDP does not, as it does not underestimate the uncertainty outside of the observed data.

Understanding the interplay between density dependent birth function and maturation time delay using a reaction-diffusion population model

The present work employs a nonlocal delay reaction-diffusion model to study the impacts of the density dependent birth function, maturation time delay and population dispersal on single species dynamics (i.e., extinction, survival, extinction-survival). It is shown that the maturation time and the birth function are two major factors determining the fate of single species. Whereas the dispersal acts as a subsidiary factor that only affects the spatial patterns of population densities. When the birth function has a compensating density dependence, maturation time delay cannot destabilize the population survival at the positive equilibrium. Nevertheless, when the birth function has an over-compensating density dependence, the population densities of single species fluctuate in the spatial domain due to the increased maturation time delay. With the Allee effect and over-compensating density dependence, the increases in the maturation time may cause extinction of the single species in the entire spatial domain. The numerical simulations suggest that the solutions of the general model may temporarily remain nearby a stationary wave pulse or a stationary wavefront of the reduced model. The former indicates the survival of single species in a narrow region of the spatial domain. Whereas the latter represents the survival in the entire left-half or right-half of the spatial domain.

Active Brownian agents with concentration-dependent chemotactic sensitivity.

We study a biologically motivated model of overdamped, autochemotactic Brownian agents with concentration-dependent chemotactic sensitivity. The agents in our model move stochastically and produce a chemical ligand at their current position. The ligand concentration obeys a reaction-diffusion equation and acts as a chemoattractant for the agents, which bias their motion towards higher concentrations of the dynamically altered chemical field. We explore the impact of concentration-dependent response to chemoattractant gradients on large-scale pattern formation, by deriving a coarse-grained macroscopic description of the individual-based model, and compare the conditions for emergence of inhomogeneous solutions for different variants of the chemotactic sensitivity. We focus primarily on the so-called receptor-law sensitivity, which models a nonlinear decrease of chemotactic sensitivity with increasing ligand concentration. Our results reveal qualitative differences between the receptor law, the constant chemotactic response, and the so-called log law, with respect to stability of the homogeneous solution, as well as the emergence of different patterns (labyrinthine structures, clusters, and bubbles) via spinodal decomposition or nucleation. We discuss two limiting cases, where the model can be reduced to the dynamics of single species: (I) the agent density governed by a density-dependent effective diffusion coefficient and (II) the ligand field with an effective bistable, time-dependent reaction rate. In the end, we turn to single clusters of agents, studying domain growth and determining mean characteristics of the stationary inhomogeneous state. Analytical results are confirmed and extended by large-scale GPU simulations of the individual based model.

Logistic type equations with discrete delay and quasi-periodic suppression rate

In this paper a single species dynamics governed by the logistic type delayed equation with an external influence (more precisely, suppression) on the population size is studied. Conditions guaranteeing global stability of the zero steady state are proved. These conditions are necessary and sufficient for a periodic or quasi-periodic suppression rate. Moreover, if the external influence is periodic and the zero steady state is repulsive, the existence of a periodic solution is shown. From the applications point of view, the logistic type equations are often used in the description of underlying tumour dynamics. In this case the external influence reflects the tumour treatment and the global stability of the zero steady state can be interpreted as the cure.

Exploitation of a single species by a threshold management policy

Continuous time models of single exploited populations usually generate outcomes expressing a dependence of yield and economic items on harvest intensity. In this work it is shown that a known threshold policy is able to generate yield and related economic items that do not depend on harvest intensity, but rather on the values of the population threshold itself and the species intrinsic parameters. It is argued that since this result can be carried over to other models of single species dynamics, it may have significant implications in the management and conservation of exploited populations.

Fluctuations of fish populations and the magnifying effects of fishing

A central and classic question in ecology is what causes populations to fluctuate in abundance. Understanding the interaction between natural drivers of fluctuating populations and human exploitation is an issue of paramount importance for conservation and natural resource management. Three main hypotheses have been proposed to explain fluctuations: (i) species interactions, such as predator-prey interactions, cause fluctuations, (ii) strongly nonlinear single-species dynamics cause fluctuations, and (iii) environmental variation cause fluctuations. We combine a general fisheries model with data from a global sample of fish species to assess how two of these hypothesis, nonlinear single-species dynamics and environmental variation, interact with human exploitation to affect the variability of fish populations. In contrast with recent analyses that suggest fishing drives increased fluctuations by changing intrinsic nonlinear dynamics, we show that single-species nonlinear dynamics alone, both in the presence and absence of fisheries, are unlikely to drive deterministic fluctuations in fish; nearly all fish populations fall into regions of stable dynamics. However, adding environmental variation dramatically alters the consequences of exploitation on the temporal variability of populations. In a variable environment, (i) the addition of mortality from fishing leads to increased temporal variability for all species examined, (ii) variability in recruitment rates of juveniles contributes substantially more to fluctuations than variation in adult mortality, and (iii) the correlation structure of juvenile and adult vital rates plays an important and underappreciated role in determining population fluctuations. Our results are robust to alternative model formulations and to a range of environmental autocorrelation.

Analysis on the critical speed of traveling waves

The note is concerned with a time-delayed reaction–diffusion equation with nonlocality for the population dynamics of single species. For the critical speed of traveling waves, we give a detailed analysis on its location and asymptotic behavior with respect to the parameters of the diffusion rate and mature age, respectively.

Climatic effects on the phenology of lake processes

AbstractPopulations living in seasonal environments are exposed to systematic changes in physical conditions that restrict the growth and reproduction of many species to only a short time window of the annual cycle. Several studies have shown that climate changes over the latter part of the 20th century affected the phenology and population dynamics of single species. However, the key limitation to forecasting the effects of changing climate on ecosystems lies in understanding how it will affect interactions among species. We investigated the effects of climatic and biotic drivers on physical and biological lake processes, using a historical dataset of 40 years from Lake Washington, USA, and dynamic time‐series models to explain changes in the phenological patterns among physical and biological components of pelagic ecosystems. Long‐term climate warming and variability because of large‐scale climatic patterns like Pacific decadal oscillation (PDO) and El Niño–southern oscillation (ENSO) extended the duration of the stratification period by 25 days over the last 40 years. This change was due mainly to earlier spring stratification (16 days) and less to later stratification termination in fall (9 days). The phytoplankton spring bloom advanced roughly in parallel to stratification onset and in 2002 it occurred about 19 days earlier than it did in 1962, indicating the tight connection of spring phytoplankton growth to turbulent conditions. In contrast, the timing of the clear‐water phase showed high variability and was mainly driven by biotic factors. Among the zooplankton species, the timing of spring peaks in the rotifer Keratella advanced strongly, whereas Leptodiaptomus and Daphnia showed slight or no changes. These changes have generated a growing time lag between the spring phytoplankton peak and zooplankton peak, which can be especially critical for the cladoceran Daphnia. Water temperature, PDO, and food availability affected the timing of the spring peak in zooplankton. Overall, the impact of PDO on the phenological processes were stronger compared with ENSO. Our results highlight that climate affects physical and biological processes differently, which can interrupt energy flow among trophic levels, making ecosystem responses to climate change difficult to forecast.

Contrasting dynamics in the same plant-herbivore interaction.

Long-term studies of two-species interactions under field conditions are unusual; most long-term field studies are of single species dynamics (1-6). Concurrent long-term studies on the dynamics of the same two interacting species in different locations are very rare. This result has led to the tacit assumption that different cases of the same two-species interaction would involve essentially quantitative differences (e.g., context-specific differences in the numeric values of demographic parameters like fecundity or death rates). Here, we show that for one of the best-known two-species systems (ragwort and cinnabar moth), this finding does not hold. The interaction between the plant and its herbivore is fundamentally different in coastal dunes in The Netherlands and in grasslands in Southeast England. In the first case, the dynamics are cyclic and the interaction involves both direct and delayed density dependence; in the second case, the insect has little impact on plant dynamics and there are no time lags in density dependence. The difference is caused by differences in the importance of seed-limitation in plant recruitment in the two locations.