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

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

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

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

Stochastic Community Assembly: Does It Matter in Microbial Ecology?

Understanding the mechanisms controlling community diversity, functions, succession, and biogeography is a central, but poorly understood, topic in ecology, particularly in microbial ecology. Although stochastic processes are believed to play nonnegligible roles in shaping community structure, their importance relative to deterministic processes is hotly debated. The importance of ecological stochasticity in shaping microbial community structure is far less appreciated. Some of the main reasons for such heavy debates are the difficulty in defining stochasticity and the diverse methods used for delineating stochasticity. Here, we provide a critical review and synthesis of data from the most recent studies on stochastic community assembly in microbial ecology. We then describe both stochastic and deterministic components embedded in various ecological processes, including selection, dispersal, diversification, and drift. We also describe different approaches for inferring stochasticity from observational diversity patterns and highlight experimental approaches for delineating ecological stochasticity in microbial communities. In addition, we highlight research challenges, gaps, and future directions for microbial community assembly research

Spatial Patterns in Plant Diversity

The present study is divided into two parts: Firstly, null models where used to test whether plant communities in a New Zealand forest were assembled deterministically or stochastically. Secondly, a relationship between a plant trait; Leaf Mass per Area (LMA) and environmental conditions was investigated in a New Zealand forest. For the first study abundance of adult species was recorded in thirty 30m x 30m plots at Otari Wilton's Bush. In a subsample of six plots, the abundance of seedling species was also recorded. Null models for species co-occurrence, species richness, species abundance and niche overlap were used in order to establish how plant communities assemble at Otari Wilton's Bush. There was evidence of both determinist and stochasticity in some aspects of the plant community, it appears that seedlings are mainly randomly assembled whereas, determinism appears to be the main driver of community composition for mature trees. Results therefore suggest a pluralistic approach should be used in order to explain plant community patterns at Otari Wilton's Bush. For the second study, of all species observed in the first study only those species found in five or more of the plots were examined. For those species, the height of the two highest individuals was measured. From each individual, six fully exposed leaves were collected and measured. Measurements of environmental conditions were also collected for all plots. Principal component analysis and multiple regression was used to analyse the data. Height related (vertical) trends were observed for three surveyed species such that LMA significantly increased with plant height. Horizontal patterns were observed for two species, and for three species it was not possible to distinguish the association of tree height (vertical) and position along the forest (horizontal) with LMA. Potentially, by including more species in future studies a clearer pattern will be observed. It could also be that different species display different strategies regarding LMA and if so, a study more focused on individual species in isolation may be able to provide more informative explanations

The ecomics of ecosystems and biodiversity: scoping the scale

The G8 decided in March 2007 to initiate a “Review on the economics of biodiversity loss”, in the so called Potsdam Initiative: 'In a global study we will initiate the process of analysing the global economic benefit of biological diversity, the costs of the loss of biodiversity and the failure to take protective measures versus the costs of effective conservation. The study is being supported by the European Commission (together with the European Environmental Agency and in cooperation with the German Government. “The objective of the current study is to provide a coherent overview of existing scientific knowledge upon which to base the economics of the Review, and to propose a coherent global programme of scientific work, both for Phase 2 (consolidation) and to enable more robust future iterations of the Review beyond 2010.

Colonisation and diversification in invertebrates: looking within species on islands to connect pattern and process

How species originate and how communities of species assemble are among the most intriguing questions in biology, and colonisation is a key element to understand them. Using two island scenarios and applying molecular tools, this thesis looks within species to investigate the themes of colonisation (both island colonisation and European continental recolonisation) and diversification processes in invertebrates. The aim was to address three gaps in our understanding about island colonisation, speciation and the assembly of biota. In the Canary Islands, an oceanic island system, the gaps addressed were: (i) the possibility that genomic admixture among multiple founding lineages has featured in the diversification of a very species rich coleopteran genus; and (ii) the lack of information regarding the colonisation history and dynamics of the small arthropod soil dwelling fauna. In Great Britain, a continental island system, the gap addressed was the under-explored possibility that the UK was not completely defaunated during glaciations, then recolonised from external sources, but that a more complex pattern, involving persistence within small cryptic refugia, may have featured in the history of its invertebrate soil dwelling fauna. I reveal two instances of shared mtDNA variation among weevil species from different Canarian islands for which I was able to dismiss explanations of incomplete lineage sorting and reveal a history of colonisation and speciation involving genetic admixture (first gap). I characterise Collembola evolutionary diversity within Tenerife and the distribution of lineage colonisation times, and reveal this fauna to be represented by a mosaic of very old lineages and a large number of very recently arrived lineages (second gap). Finally, I reveal signatures of survival and persistence of the Collembola fauna through the last Pleistocene glaciation in Great Britain (third gap). How these results fit into a broader evolutionary and conservation context as well as future directions are discussed

Ecotrophic Model for an Ecosystem Approach for Mangrove Fisheries in Thailand

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The impact of multiple stressors on coastal biodiversity and associated ecosystem services

Marine and coastal ecosystems are subject to diverse and increasingly intensive anthropogenic activities, making understanding cumulative effects critically important. However, accurately accounting for the cumulative effects of human impacts can be difficult, with the possibility of multiple stressors interacting and having greater impacts than expected, compounding direct and indirect effects on individuals, populations, communities and ecosystems. Assessment of multiple stressors therefore requires extensive scientific research that directly tests how single or multiple ecological components are affected by stressors, both singly and when combined, and as a consequence, cumulative effects assessments are now increasingly included in environmental assessments. Currently, there is a need to assess these at larger spatial scales, with additional research also urgently needed on the responses of ecological components, processes and functions to single and cumulative stressors. As cumulative environmental impacts could be better addressed by regional stressor effects assessments that combine methods for predicting multiple pressures on ecosystem recovery alongside degradation, this study used several separate approaches that can be used in parallel to give support for local management measures. I tested four completely different methods – a range of multi-metric indices, a food web model (Ecopath), a predictive model (Ecosim) and a Bayesian Belief Network model. Each approach was tested and compared in two shallow water estuarine systems, in Scotland and England, initially concerning the impact of nutrient enrichment and subsequent recovery and was followed by an investigation of how the addition of multiple stressors (nutrient levels, temperature and river-flow rates) would impact the future state of each system. The response to stressors was highly context dependent, varying between and within geographic locations. Overall, each of the four different approaches complemented each other and gave strong support for the need to make big reductions in the pressures and to consider trade-offs between impacting pressures. The models and tools also indicate that in order to reach an improved overall environmental state of each ecosystem, a focus on nutrient reductions are likely to be the most effective of the controls on stressors explored and that cumulative effects of the management of nutrient inputs and increased water temperatures and river-flow are likely to exist