Ecological Complexity最新文献

We present a continuous time predator-prey model and predator’s growth subjected to component Allee effect. The model also includes density dependent mortality of predator. We investigate our model both analytically and numerically, and highlighted the effect of density independent mortality and Allee effect. In our system, we find that a fixed point representing the extinction of predator is always a stable point. When coexistence equilibria exists our system is bistable. We have observed that tristability is possible for our model that includes two stable co-existence fixed point. The most important phenomena which we have observed are hydra effect and cascading effect. Due to component Allee effect in predator the system shows multiple hydra effect. We discuss the phenomenon of bubbling, which indicates increasing and decreasing of amplitudes of cycles. We have presented one-parametric as well as two-parametric bifurcation diagram and also all possible bifurcations that the system could go through.

Over the last several years, the IUCN Red List approach for assessing the risk of extinction faced by species has been adapted into a Red List of Ecosystems methodology. This endeavor faces several important challenges, including how to define the types of ecosystems to which the Red List criteria are applied, and how to manage information on the geographic distribution of ecosystems in an open, transparent, and standardized manner linking mapping, typology, and field studies. We propose a fundamentally novel approach that differs from currently available ecosystem typologies in three important aspects by (1) offering a new way of conceptualizing types of ecosystems, (2) providing an explicit method for communicating the conceptualized ecosystems and how they are circumscribed, and (3) developing technical tools for managing the resulting conceptual model. Firstly, ecosystem types are defined by studying biogeoclimatic gradients using an approach that is both modular (in which combinations of ecological factors are studied at a given scale) and hierarchical (involving relative spatial and temporal scales in which local/site gradients are dependent on bioclimatic/regional gradients). This avoids the problem of classes that are not mutually exclusive and enables the classification of all types of ecosystems, including for example marshes on rocky outcrops in superhumid tropical montane areas. Secondly, the names of ecosystem species are linked to a nomenclatural type defined by a ‘type site’ or ‘biotype’, adopting a principle that makes clear a given author's notion of an ecosystem type even if the accompanying name and description are partial or imperfect, or when the ecosystem type is delimited too broadly according to the interpretation of another author. Ecosystem names are structured as a descriptive diagnosis based on a standardized set of characters and character states. This typological approach for facilitating the naming and comparison of ecosystem circumscriptions is thus truly taxonomic in nature. Thirdly, in order to facilitate the use and application of the conceptual approach presented here, we translate it into a practical tool by developing a smartphone-based system to collect data for observing and describing virtual ecosystem specimens in the field, along with the "Bio" database, which manages ecosystem data and also enables tracking synonymies using an open system that entails assigning determinavits to biotypes.

The changing climate is expected to alter the timings of key events in species life-histories. These shifts are likely to have important consequences for infectious disease dynamics, as the distribution and abundance of host species will lead to a different environment for parasites. Previous work has shown how seasonality in single host traits - most commonly the reproduction rate or transmission rate - can lead to an array of complex epidemiological dynamics, including chaos and multiple-stable states, with changes to the timing and amplitude of the seasonal peaks often driving drastic changes in behaviour. However, more than one life-history trait is likely to be seasonal, and changing environmental conditions may impact each of them in different ways, yet there have been few studies of host-parasite dynamics that include more than one seasonal trait. Here we examine a Susceptible-Infected-Recovered epidemiological model in which both reproduction and transmission exhibit seasonal fluctuations. We examine how the amplitude and timing of these seasonal peaks impact disease dynamics. We show that the relative timing of the two events is key, with the most stable dynamics when births peak a few months before transmission. We also show that chaotic dynamics become more likely when transmission in particular has a high amplitude, and when baseline transmission and virulence are high. Our results emphasise the importance of seasonality and timing of host life-history events to disease dynamics.

An epidemiological model based on probabilistic cellular automaton is proposed to investigate the dynamics of two co-circulating infections. In the model, one of these two diseases compromises the immune response to future infections; however, there is vaccine against this immunosuppressive disease. The goal is to evaluate the impact of the vaccination coverage on the prevalence and on the cumulative deaths associated with both contagious diseases. The performed numerical simulations highlight the importance of vaccination on decreasing morbidity and mortality. The results are discussed from a public health standpoint, by taking into account outbreaks of measles and COVID-19.

Excessive infrastructural developments, driven by urbanization, have not only brought destruction of forests, but also exacerbated the temperature of cities (or towns) due to formation of urban heat islands. Keeping such an urban system in mind, a nonlinear dynamical model is formulated in the proposed work in terms of system of differential equations. The model, comprising of forest resources, human population, urban infrastructural developments and temperature as system variables, is formulated on the assumption that infrastructural developments, induced through human population, escalate temperature of the region at the cost of deforestation. The derived model is mathematically analyzed for qualitative properties of its equilibrium solutions, extending from their existences to stabilities. Further, to demonstrate the impact of parametric variations on dynamical behavior, the system is also investigated for transcritical and Hopf - bifurcations. Quantitative analysis is also being executed with available numerical data to substantiate qualitative findings and to determine sensitiveness of equilibrium values of model outcomes towards system parameters. The results reveal that any of the parameters, which directly or indirectly, responsible for escalation in temperature of the region can put the system in a state of periodic oscillations, arises through Hopf - bifurcation. Therefore, it is suggested to control urban infrastructural developments through implementation of government strategies, which should include check over illegal encroachment of forested land for infrastructural developments.

The capacity of highly diverse systems to prevail has proven difficult to explain. In addition to methodological issues, the inherent complexity of ecosystems and issues like multicausality, non-linearity and context-specificity make it hard to establish general and unidirectional explanations. Nevertheless, in recent years, high order interactions have been increasingly discussed as a mechanism that benefits the functioning of highly diverse ecosystems and may add to the mechanisms that explain their persistence. Until now, this idea has been explored by means of hypothetical simulated networks. Here, we test this idea using an updated and empirically documented network for a coffee agroecosystem. We identify potentially key nodes and measure network robustness in the face of node removal with and without incorporation of high order interactions. We find that the system's robustness is either increased or unaffected by the addition of high order interactions, in contrast with randomized counterparts with similar structural characteristics. We also propose a method for representing networks with high order interactions as ordinary graphs and a method for measuring their robustness.

In the present study, a new neural network-based terminal sliding mode technique is proposed to stabilize and synchronize fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is implemented to estimate unknown functions of the system. Moreover, through the proposed Chebyshev neural network observer, the effects of external disturbances are fully taken into account. The weights of the Chebyshev neural network observer are adjusted based on adaptive laws. The finite-time convergence of the closed-loop system, which is a new concept for ecological systems, is proven. Then, the dependency of the system on the value of the fractional time derivatives is investigated. Lastly, the proposed control scheme is applied to the fractional-order ecological system. Through numerical simulations, the performance of the developed technique for synchronization and stabilization are assessed and compared with a conventional method. The numerical simulations strongly corroborate the effective performance of the proposed control technique in terms of accuracy, robustness, and convergence time for the unknown nonlinear system in the presence of external disturbances.

Early-warning signals of a regime shift (EWS) indicate, for a wide range of systems, if a tipping-point is being approached. In ecology, EWS are well established from a theoretical perspective but are far from unequivocal when applied to field data. The gap between theory and application is caused by a set of limitations, like the lack of coherence between different EWS, data acquisition issues, and false results. Experiments assessing EWS may provide an empirical mechanistic understanding of why an EWS was observed (or failed to be observed), which often cannot be elucidated by simple computational modeling or pure environmental data. Here we focused on aquatic experiments to explore to what extent the existing EWS experiments can bridge the gap between the theory and real-world application. For that, we used the Thomson-ISI Web of Science© database to retrieve EWS experiments executed before early-2020, detailing their experimental designs and each EWS assessed. Success rates - correct anticipation of tipping points – were around 70% for the most used EWS (assessment of autocorrelation, variance, recovery, and shape of the distribution using abundance, Chlorophyll-a, Phycocyanin, and dissolved oxygen data). Yet, no EWS showed to be 100% reliable, and their use demands cautious interpretation. As a rule, we observed that experiments were not designed to tackle issues encountered in real-world situations. They lack a deep mechanistic understanding of why, when, and how an EWS was observed or not. When experiments did aim to assess issues encountered in the real world, the experimental designs were often of low ecological significance. We also investigated the relationship between sampling and the success rate of EWS, observing that the sampling regime might have to be tailor-made towards specific monitoring objectives. Moreover, experiments have taught us that the use of EWS can be more versatile than expected, going from monitoring the extinction of single populations to the anticipation of transient regime shifts. Most of the experiments presented here supported empirical proof of the existence of EWS in aquatic systems. Still, to bridge the gap between theory and application, experiments will have to move closer to real-world conditions and better support a mechanistic understanding of why EWS may succeed or fail to anticipate a regime shift. For that, we provide six elements to take into account when designing experiments that could enhance the capabilities of EWS to go beyond the stage of proof-of-concept.

Climate change is expected to alter biological phenomena across the world, including the numbers and distributions of species and the timing of significant events in their life cycles such as reproduction and migration. Understanding how species will respond to future climate change is essential for effective wildlife management and conservation. Accordingly, in this research, we advanced the understanding of avian ecology by developing a framework for how climate change affects birds. In the first step, we evaluated the vulnerability of 537 species to climate change based on the distribution, physiology, phenology, biotic interactions, and protection status of the species in Iran. Then, we used MaxEnt models to predict the potential changes in the ranges of vulnerable species due to climate change in the next 70 years. In the third step, hotspots for birds under current and future conditions were identified using an ensemble forecasting framework and the potential changes in the hotspots in the next 70 years were predicted. Results of the climate vulnerability evaluation showed that around 40% of bird species in Iran are highly vulnerable. Our results showed that small parts of suitable habitats are currently located within protected areas. Moreover, the results showed that even smaller portions of suitable habitats will fall within protected areas in the future. The reduced coverage in the future will diminish the benefits of protected areas for the species and make the species more vulnerable to climate change. These results can be used by wildlife managers to identify areas with protection priority, and for prediction of corridors, core habitats, and new areas to establish protected areas in the future.

To deal with real-life diversity of our ecosystem, this paper analyzes two prey-two predator model including both Type-I and Type-II functional responses. The interior equilibrium point of the proposed model is calculated; and behaviour of the model around that point is studied. Local stability at an interior equilibrium point is discussed; and possibility of Hopf-bifurcation with probable direction is studied. A generalized form of the Poincaré-Bendixon criterion is applied to establish the sufficient conditions for global stability of the proposed model surrounding at an interior equilibrium point. Numerical simulations are also conducted in support of our work. Conclusions of our findings and some probable future directions are also included at the end.