genomic and behavioural evolution in the artificial ecosystem simulation EcoSim

Artificial life evolutionary systems facilitate addressing lots of fundamental questions in evolutionary genetics. Behavioral adaptation requires long term evolution with continuous emergence of new traits, governed by natural selection. We model organism\u27s genomes coding for their behavioral model and represented by fuzzy cognitive maps (FCM), in an individual-based evolutionary ecosystem simulation (EcoSim). The emergent of new traits (genes) in EcoSim is examined by studying their effect on individual\u27s fitness and well being. We examine how the new traits are used to predict the value of fitness using machine learning techniques. A comparison between the genomic evolution of EcoSim and a neutral model (a randomized version of EcoSim) is examined focusing on their respective genomic diversity. In order to further emphasize the importance of genetic diversity to adaptation and thus the well being of individuals, we were encouraged to study the effect that genetic diversity has on fitness. EcoSim gives us the chance to study the relation between species genetic diversity and average species fitness without the limits in environmental conditions and time scales found in biological studies, but in highly variable environments and across evolutionary time. The ecological effects of predator removal and its consequence on prey behavior have been investigated widely. We investigated the effects of predation risk on prey energy allocation and fitness. Here the role of predator removal on the contemporary evolution of prey traits such as movement, reproduction and foraging was evaluated. Our study clearly shows that predation risk alone induces behavioural changes in prey which drastically affect population and community dynamics, A classification algorithm was used to demonstrate the difference between genomes belonging to prey co-evolving with predators and prey evolving in the absence of predation pressure. We argue that predator introductions to naive prey might be destabilizing if prey have evolved and adapted to the absence of predators. Our results suggest that both predator introduction and predator removal from an ecosystem have widespread effects on the survival and evolution of prey by altering their genomes and behaviour, even after relatively short time intervals

Simulations and Modelling for Biological Invasions

Biological invasions are characterized by the movement of organisms from their native geographic region to new, distinct regions in which they may have significant impacts. Biological invasions pose one of the most serious threats to global biodiversity, and hence significant resources are invested in predicting, preventing, and managing them. Biological systems and processes are typically large, complex, and inherently difficult to study naturally because of their immense scale and complexity. Hence, computational modelling and simulation approaches can be taken to study them. In this dissertation, I applied computer simulations to address two important problems in invasion biology. First, in invasion biology, the impact of genetic diversity of introduced populations on their establishment success is unknown. We took an individual-based modelling approach to explore this, leveraging an ecosystem simulation called EcoSim to simulate biological invasions. We conducted reciprocal transplants of prey individuals across two simulated environments, over a gradient of genetic diversity. Our simulation results demonstrated that a harsh environment with low and spatially-varying resource abundance mediated a relationship between genetic diversity and short-term establishment success of introduced populations rather than the degree of difference between native and introduced ranges. We also found that reducing Allee effects by maintaining compactness, a measure of spatial density, was key to the establishment success of prey individuals in EcoSim, which were sexually reproducing. Further, we found evidence of a more complex relationship between genetic diversity and long-term establishment success, assuming multiple introductions were occurring. Low-diversity populations seemed to benefit more strongly from multiple introductions than high-diversity populations. Our results also corroborated the evolutionary imbalance hypothesis: the environment that yielded greater diversity produced better invaders and itself was less invasible. Finally, our study corroborated a mechanical explanation for the evolutionary imbalance hypothesis – the populations evolved in a more intense competitive environment produced better invaders. Secondly, an important advancement in invasion biology is the use of genetic barcoding or metabarcoding, in conjunction with next-generation sequencing, as a potential means of early detection of aquatic introduced species. Barcoding and metabarcoding invariably requires some amount of computational DNA sequence processing. Unfortunately, optimal processing parameters are not known in advance and the consequences of suboptimal parameter selection are poorly understood. We aimed to determine the optimal parameterization of a common sequence processing pipeline for both early detection of aquatic nonindigenous species and conducting species richness assessments. We then aimed to determine the performance of optimized pipelines in a simulated inoculation of sequences into community samples. We found that early detection requires relatively lenient processing parameters. Further, optimality depended on the research goal – what was optimal for early detection was suboptimal for estimating species richness and vice-versa. Finally, with optimal parameter selection, fewer than 11 target sequences were required in order to detect 90% of nonindigenous species

Investigation of sympatric speciation as the outcome of competition for food resource by means of an individual-based modeling approach

Sympatric speciation, the emergence of new species in the absence of geographic isolation, is one of the most controversial issues in evolutionary biology. Although today the plausibility of the occurrence of sympatric speciation is theoretically acknowledged, its underlying mechanisms are still unknown. We applied a modeling approach with three trophic levels (primary resource, prey, and predator) and supplied prey species with two different food resources (Food 1 and Food 2) to track prey lineage through evolutionary time to detect any indicators of the occurrence of sympatric speciation caused by specialized food consumption. Whereas, Food 1 was the more available resource, Food 2 had higher energy content. Initially, when there was not yet any specific food specialization, Food 1 consumption rate was significantly higher compared to Food 2. Eventually, around time step 22,000 and after the emergence of food consumption specialization, the exploitation of Food 2 was higher than Food 1 in spite of the fact that prey individuals were more frequently encountered with Food 1 than Food 2. Drawing a comparison between simulations with only one food resource and simulations with two available food resources revealed that complete reproductive isolation caused by disruptive selective pressure exerted by adaptation to different resources plays a curial role in the emergence of sympatric species. Machine learning techniques were also employed to identify the shared patterns among sympatric species. Results showed that for most lineages sympatric divergence has occurred at the beginning of the process of the emergence of specialized use. If not, these species have possessed a high spatial distribution and had to meet two conditions to be diverged sympatrically: i. high genetic diversity and ii. a large population size

Variational Autoencoder Based Estimation Of Distribution Algorithms And Applications To Individual Based Ecosystem Modeling Using EcoSim

Individual based modeling provides a bottom up approach wherein interactions give rise to high-level phenomena in patterns equivalent to those found in nature. This method generates an immense amount of data through artificial simulation and can be made tractable by machine learning where multidimensional data is optimized and transformed. Using individual based modeling platform known as EcoSim, we modeled the abilities of elitist sexual selection and communication of fear. Data received from these experiments was reduced in dimension through use of a novel algorithm proposed by us: Variational Autoencoder based Estimation of Distribution Algorithms with Population Queue and Adaptive Variance Scaling (VAE-EDA-Q AVS). We constructed a novel Estimation of Distribution Algorithm (EDA) by extending generative models known as variational autoencoders (VAE). VAE-EDA-Q, proposed by us, smooths the data generation process using an iteratively updated queue (Q) of populations. Adaptive Variance Scaling (AVS) dynamically updates the variance at which models are sampled based on fitness. The combination of VAE-EDA-Q with AVS demonstrates high computational efficiency and requires few fitness evaluations. We extended VAE-EDA-Q AVS to act as a feature reducing wrapper method in conjunction with C4.5 Decision trees to reduce the dimensionality of data. The relationship between sexual selection, random selection, and speciation is a contested topic. Supporting evidence suggests sexual selection to drive speciation. Opposing evidence contends either a negative or absence of correlation to exist. We utilized EcoSim to model elitist and random mate selection. Our results demonstrated a significantly lower speciation rate, a significantly lower extinction rate, and a significantly higher turnover rate for sexual selection groups. Species diversification was found to display no significant difference. The relationship between communication and foraging behavior similarly features opposing hypotheses in claim of both increases and decreases of foraging behavior in response to alarm communication. Through modeling with EcoSim, we found alarm communication to decrease foraging activity in most cases, yet gradually increase foraging activity in some other cases. Furthermore, we found both outcomes resulting from alarm communication to increase fitness as compared to non-communication

Investigating The Effects of Food Chain on Sympatric Speciation Using ECOSIM

The study of sympatric speciation in evolutionary biology is facing the obstacle of unifying empirical studies with existing theoretical investigations. Disruptive selection due to preferential food resource usage is considered as the main hypothesis to explain the sympatric speciation occurrence in empirical studies. We extend an individual based evolving predator-prey ecosystem platform called EcoSim [Gras et al. 2009a] to model a dual resource system. We investigated whether and in which conditions the selective pressures acting on foraging behaviors drove sympatric speciation. We observed clear results showing some behavioral modifications occurring as a consequence of preferential resource usage. We also observed many cases where the sympatric speciation criteria described in the literature were fulfilled. Using several machine learning techniques, we extracted explicit rules that can predict with a very high accuracy the occurrence of sympatric speciation based on ecological factor observations. Moreover, we confirmed that the existence of a second food resource is determinant for the emergence of sympatric phenomenon. We also proved that our method is able to discover very generic rules which may later be used to structure empirical studies

Individual-Based Modeling and Data Analysis of Ecological Systems Using Machine Learning Techniques

Artificial life (Alife) studies the logic of living systems in an artificial environment in order to gain a deeper insight of the complex processes and governing rules in such systems. EcoSim, an Alife simulation for ecological modeling, is an individual-based predator-prey ecosystem simulation and a generic platform designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes in biology and ecology. Speciation and extinction of species are two essential phenomena in evolutionary biology. Many factors are involved in the emergence and disappearance of species. Due to the complexity of the interactions between different factors, such as interaction of individuals with their environment, and the long time required for the observation, studying such phenomena is not easy in the real world. Using data sets obtained from EcoSim and machine learning techniques, we predicted speciation and extinction of species based on numerous factors. Experimental results showed that factors, such as demographics, genetics, and environment are important in the occurrence of these two events in EcoSim.We identified the best species-area relationship (SAR) models, using EcoSim, along with investigating how sampling approaches and sampling scales affect SARs. Further, we proposed a machine learning approach, based on extraction of rules that provide an interpretation of SAR coefficients, to find plausible relationships between the models\u27 coefficients and the spatial information that likely affect SARs. We found the power function family to be a reasonable choice for SAR. Furthermore, the simple power function was the best ranked model in nested sampling amongst models with two coefficients. For some of the SAR model coefficients, we obtained clear correlations with spatial information, thereby providing an interpretation of these coefficients. Rule extraction is a method to discover the rules explaining a predictive model of a specific phenomenon. A procedure for rule extraction from Random Forest (RF) is proposed. The proposed methods are evaluated on eighteen UCI machine learning repository and four microarray data sets. Our experimental results show that the proposed methods outperform one of the state-of-the art methods in terms of scalability and comprehensibility while preserving the same level of accuracy

EcoDemics, Modelling Epidemic Spread in a Simulated Predator-Prey Evolutionary Ecosystem

Modeling the progress of an epidemic in a population has received significant attention among various fields of science. Many epidemiological models assume random mixing of the population, homogeneous hosts, and a static environment. We are interested in modeling epidemic spread in a dynamic evolving ecosystem with behavioral models associated to its individuals. To this end, we present EcoDemics; which integrates the classical SIR (Susceptible-Infected-Removed) disease model to an individual-based evolutionary predator-prey ecosystem simulation, EcoSim. The behavioral model of each agent in EcoDemics is based on a fuzzy cognitive map (FCM) that determines the heterogeneous interactions between individuals. We present the disease model used and we demonstrate how the epidemic spread in a random mixing ecosystem differs from a heterogeneous ecosystem with its behavioral model. We observed that dynamics of the ecosystem, along with the spatial distribution of agents, play a significant role in disease progression. Due to the high mitigation capacity and significance of the immunization intervention, we explore vaccination techniques with various time delays and population proportions in EcoDemics. Based on the herd immunity theory, the whole population can be protected against a contagious disease by vaccination of a fraction of individuals. We investigate this principle in EcoDemics and compare our results with real epidemics data. A number of mathematical simulations have been used to analyze host-pathogen dynamics in the presence of predators; however, to the best of our knowledge, this is the first individual-based modeling study exploring the effect of predators on prey infection dynamics in a predator-prey ecosystem simulation. We used the EcoDemics framework to investigate the effect of predation on infection dynamics in EcoDemics. Our results are in agreement with both numerical and field studies

Studying Mate Choice and Species Extinction using EcoSim

In this thesis, we tackle the two biological questions of Mate Choice and Species Extinction using a predator-prey ecosystem simulation named EcoSim. We conducted two experiments to studies these questions. In the first experiment, the impact of three mate choice models, random, similar, and exaggerated ornamental mate, on the behaviour and characteristic of individuals is studied. Experimental results showed very interesting genetic and behavioural differences between each model. In the second experiment, the effect of demographic, environmental, and genetic factors on the species extinction was investigated. Experimental results illustrated that some combinations of these factors can lead to predict the near future extinction with some interesting insights about the effect of these factors

Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology

One approach to understanding the behaviour of complex systems is individual-based modeling, which provides a bottom-up approach allowing for the consideration of the traits and behaviour of individual organisms. Ecosystem models aim to characterize the major dynamics of ecosystems, in order to synthesize the understanding of such systems and to allow predictions of their behaviour. Moreover, ecosystem simulations have the potential to help scientists address theoretical questions as well as helping with ecological resource management. Because in reality biologists do not have much data regarding variations in ecosystems over long periods of time, using the results of ecological computer simulation for making reasonable predictions can help biologists to better understand the long-term behaviour of ecosystems. Different versions of ecosystem simulations have been developed to investigate several questions in ecology such as how speciation proceeds in the absence of experimenter-defined functions. I have investigated some of these questions relying on complex interactions between the many individuals involved in the system, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. Most scientists now believe that natural phenomena have to be looking as a chaotic system. In the past few years, chaos analysis techniques have gained increasing attention over a variety of applications. I have analyzed results of complex models to see whether chaotic behaviour can emerge, since any attempt to model a realistic system needs to have the capacity to generate patterns as complex as the ones that are observed in real systems. To further understand the complex behaviour of real systems, a new algorithm for long-term prediction of time series behaviour is also proposed based on chaos analysis. We evaluated the performance of our new method with respect to the prediction of the Dow-Jones industrial index time series, epileptic seizure and global temperature anomaly

Understanding the individual to implement the ecosystem approach to fisheries management

Ecosystem-based approaches to fisheries management (EAFMs) have emerged as requisite for sustainable use of fisheries resources. At the same time, however, there is a growing recognition of the degree of variation among individuals within a population, as well as the ecological consequences of this variation. Managing resources at an ecosystem level calls on practitioners to consider evolutionary processes, and ample evidence from the realm of fisheries science indicates that anthropogenic disturbance can drive changes in predominant character traits (e.g. size at maturity). Eco-evolutionary theory suggests that human-induced trait change and the modification of selective regimens might contribute to ecosystem dynamics at a similar magnitude to species extirpation, extinction and ecological dysfunction. Given the dynamic interaction between fisheries and target species via harvest and subsequent ecosystem consequences, we argue that individual diversity in genetic, physiological and behavioural traits are important considerations under EAFMs. Here, we examine the role of individual variation in a number of contexts relevant to fisheries management, including the potential ecological effects of rapid trait change. Using select examples, we highlight the extent of phenotypic diversity of individuals, as well as the ecological constraints on such diversity. We conclude that individual phenotypic diversity is a complex phenomenon that needs to be considered in EAFMs, with the ultimate realization that maintaining or increasing individual trait diversity may afford not only species, but also entire ecosystems, with enhanced resilience to environmental perturbations. Put simply, individuals are the foundation from which population- and ecosystem-level traits emerge and are therefore of central importance for the ecosystem-based approaches to fisheries management