Browsing by Author "Nnakenyi, Chinenye Assumpta"
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- ItemComplexity and stability of mutualistic local networks and meta-networks(Stellenbosch : Stellenbosch University, 2021-03) Nnakenyi, Chinenye Assumpta; Hui, Cang; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.ENGLISH ABSTRACT: Biotic interactions, either in local networks or in meta-networks, are ubiquitous in nature. Species interact with other species of different interaction strengths in the ecosystem. For example, mutualistic interactions, whereby species benefit from each other, have been found to play a significant role in the function and structure of ecological communities. Previous empirical and theoretical studies have shown the vital contribution of mutualistic interactions in maintaining diversity amidst perturbations from the environment. Such perturbations affect the species and their interactions, exerting pressure on the ecosystem. However, it is unclear how the strengths of species interactions affect species abundances in the communities, and understanding the mechanism behind the complexity and stability of mutualistic meta-networks and local networks remains a challenge to be addressed. In this thesis, using a random matrix approach, I found that the stability criteria of a block-structured network or matrix is obtained from max( r1; r2) m < 0, where m is derived from the expectation of the diagonal elements of the matrix, while r1 and r2 are derived from the off-diagonal elements of the matrix when the expectation of the off-diagonal elements is different from zero and equal to zero respectively. Also, using a Lotka-Volterra model of mixed interaction types in different proportions, that describes the dynamics of species abundances, I found that species abundances are determined more by the species’ sensitivities to the interaction pressures from their partners than by species’ impacts on their partners. Besides, the abundances of the rarest species was found to be a good indicator of the resilience of the communities. Even when modelling real mutualistic local networks using a modified Lotka-Volterra model that incorporates adaptive interaction switching (AIS) and environmental variables, I found that the AIS could destabilise the local networks. However, to explain the emergence of nestedness and modularity in those networks, I found AIS to be a key driving mechanism behind community nestedness, with the environmental variables playing a secondary role in explaining nestedness and modularity. Finally, using a competition-mutualism model of meta-networks, I showed the role of dispersal and the role of mutualism to the complexity and stability of the networks. I found that incorporating mutualism in the model of meta-networks is crucial to the functioning of the meta-networks, as mutualism increases the stability of the meta-networks, increases the total abundance of species, decreases unevenness in the species abundances, and increases nestedness more than in the model without mutualism. Also, I showed that dispersal is a strong stabilising factor for the meta-networks. Importantly, dispersal heterogeneity between local networks drives the changes in total abundance, unevenness, and compositional similarity of species in the meta-networks and the local networks, irrespective of the dispersal heterogeneity across species. That is dispersal heterogeneity between the local networks decreases total abundance, increases unevenness and decreases compositional similarity in the meta-networks and local networks. Knowledge about the dispersal rates between local networks and across species is crucial to understand the complexity and stability of the local and meta-networks. Hence, these findings have contributed to the stability and complexity of ecological networks, at both local and regional scales, which is relevant for the management and conservation of interaction networks with the objective of preserving the species functions and services in the ecosystem.
- ItemStructural Emergence in Mutualistic Networks(Stellenbosch : Stellenbosch University, 2016-12) Nnakenyi, Chinenye Assumpta; Hui, Cang; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.ENGLISH ABSTRACT: Mutualistic interactions are vital in sustaining species, maintaining the functions and services of the ecosystem. Network structures such as nestedness and modularity have being reported to emerge as a result of the mutualistic interactions. Although these structures have being found to have effect in the stability of mutualistic communities, mechanisms that lead to the emergence of these structures are not fully understood. From the observed pollination data of 10 Galápagos Islands, we use a modified Lotka-Voltera model of mutualism that incorporates species competition, functional responses and adaptive rewiring (Adaptive Interaction Switching [AIS] model) to predict the observed network structures. From the AIS model, almost 40% variation of the observed nestedness and more than 50% variation of the observed modularity was explained. Furthermore, using a Generalized Linear Model (GLM), the effect of environmental variables such as geographic factors (island area, isolation, age and maximum elevation) and anthropogenic factors (sampling effort and human population size) were considered together with the AIS model. The GLM can account for more than 78% variation of the observed nestedness and more than 85% of the observed modularity, with island area, isolation, sampling effort and human population size the most important variables, contributing significantly to the observed network structures. Therefore, pollination networks on Galápagos Islands are structured. The AIS model implemented can explain an appreciable level of network structure. Together with environmental variables, the results echo the importance of island area and isolation (as of island biogeography), human disturbance, sampling effort, as well as the adaptive rewiring (ecological fitting), as a candidate model for mutualistic network emergence.