Thesis (Ph.D.)--Kent State University, 2009. / Title from PDF t.p. (viewed April 14, 2010). Advisor: Mark W. Kershner. Keywords: biodiversity; nitrogen; fertilization; plant litter; trophic dynamics; epigeal community. Includes bibliographical references.

Thesis (Ph. D.)--University of Rhode Island, 2005. / Typescript. Includes bibliographical references (leaves 167-174).

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2013. / Species richness and estimates of species richness were calculated based on assemblages of bats, within the biomes of South Africa, Lesotho and Swaziland following the vegetation classification of Mucina and Rutherford (2006). Similarity indices were used to explore the various relationships between the assemblages between the various biomes. Taxonomic diversity and distinctness examined the various assemblages within each of the biomes to investigate which biomes contained assemblages that were taxonomically diverse and/or taxonomically distinct compared to all species known to occur within South Africa, Lesotho and Swaziland. The Desert biome had the lowest recorded species richness (5 species), and there was insufficient data to calculate estimates of species richness for this biome. While the Albany had 11 species recorded with species estimates (Est.) ranging between 11-12, then in increasing order - Nama-Karoo (12 species, Est. 13-25 species), Succulent-Karoo (13 species, Est. 15-30 species), Fynbos (17 species, Est. 18-25 species), Indian Ocean Coastal Belt (31 species, Est. 32-36 species), Forest (32 species, Est. 37-46 species), Grassland (39 species, Est. 42-54 species), Azonal (45 species, Est. 49-63 species) and Savanna (57 species, Est. 59-67 species). The mean recorded estimates (based on the averages of all models) and rounding up to a full species indicates that the Albany biome contains the lowest expected species richness of 12 species, then Fynbos and Nama-Karoo (21 species), Succulent-Karoo (22 species), Indian Ocean Coastal Belt (34 species), Forest (43 species), Grassland (49 species), Azonal (54 species) and Savanna (64 species). Sample completeness was calculated for each of the biomes which indicates in ascending order that the Albany biome is 93.2% complete followed by the Indian Ocean Coastal Belt biome (91.1%), Savanna biome (89.9%), Azonal biome (84.1%), Fynbos biome (81.5%), Grassland biome (80.7%), Forest biome (75.8%), Succulent-Karoo biome (61.3%), and Nama-Karoo biome (59.9%). This showed that the Albany biome was found to be the only biome that has been sufficiently sampled. The Jaccard and Sørensen pair wise indices resulted in the clustering of the biomes with similar species richness, due to the large range in species richness (5-57 species) between the biomes. The Lennon et al. (2001) index which is not affected by large species richness between the samples indicated that the Desert and Nama-Karoo assemblages were most dissimilar to one another, while the Indian Ocean Coastal Belt assemblage was the most similar to the remaining biome assemblages. The Albany biome assemblage and Azonal biome assemblage were shown to the most dissimilar to one another. The Grassland, Nama-Karoo and Savanna biomes contribute to higher taxonomic diversity, while the Albany, Azonal, Fynbos, Nama-Karoo and Succulent-Karoo biomes contain lower species richness generally but represent a higher taxonomic distinctness from the chiroptera assemblages in the Grassland and Savanna biomes. The Desert, Forest and Indian Ocean Coastal Belt biomes do not iv contain bat assemblages that are neither taxonomically distinct nor diverse when compared to the taxa of South Africa, Lesotho and Swaziland. The bat assemblage within the Nama-Karoo are both taxonomically diverse and distinct from chiroptera assemblages found within the other nine biomes, requiring a greater focus on conservation actions for the bat species assemblage located within this biome.

The complexity of ecological and evolutionary processes that govern species distributions has long presented a challenge to understanding community assembly history. The work presented here develops a conceptual framework for integrating phylogenetics and biogeography to reconstruct the assembly of communities, provides empirical support for the broad applicability of this framework, tests whether morphology can serve as a proxy for behavioral ecology, and develops a novel metric of assemblage vulnerability and shows how vulnerability is related to biogeographic history. This dissertation demonstrates the need to merge evolution and ecology to reconstruct community assembly, and provides a framework for doing so. Further, the findings presented here suggest that such an interdisciplinary approach has the potential to both reveal fundamental processes shaping the assembly of natural systems, and to illuminate the functions and properties of ecosystems based on the evolutionary histories of their constituent species.

This research is a case study evaluation of the use of self-organising map (SOM) techniques for ecosystem modelling to overcome the perceived inadequacies with conventional ecological data analysis methods. SOMs provide an analytical method within the connectionist paradigms of artificial neural networks (ANNs), developed from concepts that evolved from late twentieth century neuro-physiological experiments on the cortex cells of the human brain. The rate and extent at which humans influence environmental deterioration with commensurate biodiversity loss is a cause for major concern and to prevent further degradation by human impact, parsimonious models are urgently needed. Indeed, the need for better modelling techniques has never been so great. Ecologists and many national and international bodies see the situation as 'significantly critical' for the conservation of our global ecosystem to foster the continued wellbeing of humanity on this earth.The thesis investigates and further refines SOM based exploratory data analysis methods for modelling naturally evolving, highly diverse and extremely complex ecosystems. Earlier studies provide evidence on SOM ability to analyse complex forest and freshwater biological community structures at limited scales. On the other hand, growing concerns over conventional methods, their soundness and ability to model large volumes of data are seen as of little use, leading to arguments on the results derived from them. Case study chapters illustrate how SOM methods could be best applied to analyse often 'cryptic' ecosystems in a manner similar to that applied in modelling highly complex and diverse industrial system dynamics. Furthermore, SOM based data clustering methods, used for financial data analysis are investigated for integrated analysis of ecological and economic system data to study the effects of urbanisation on natural habitats.SOM approaches prove to be an excellent tool for analysing the changes within physical system variables and their effects on the biological systems analysed. The Long Bay-Okura Marine Reserve case study elaborates on how SOM based approaches could be best applied to model the reserve's intertidal zone with available numeric data. SOM maps depicted the characteristic microclimate within this zone from ecological monitoring data of physical attributes, without any geographical data being added. This kind of feature extraction from raw data is found to be useful and is applied to two more case studies to study the slow variables of ecosystems, such as population dynamics, and to establish their correlation with environmental variations. SOM maps are found to be capable of distinguishing the human induced variations from that of natural/ global variations, at different scales (site, regional and global) and levels using regional and global data. Hence, SOM approaches prove to be capable of modelling complex natural systems incorporating their spatial and temporal variations using the available monitoring data, this is a major advantage observed with SOM analyses.In the third case study, potential use of SOM techniques to analyse global trends on the effects of urbanisation in environmental and biological systems are explored using the World Bank's statistical data for different countries. Many state and international institutions, concerned over global environmental issues, have made attempts to develop indicators to assess the conditions of different ecosystems. The enhancements with SOM approaches against the currently recommended indicator system based on information pyramid and pressure-state-response (PSR) models are elaborated upon.The research results of SOM methods for ecosystem modelling, similar to that applied to industrial process modelling and financial system analysis show potential. SOM approaches (i.e. cluster, dependent component, decision system and trajectories/ time series analyses) provide a means for feature extraction from the available numeric data at different levels and scales, fulfilling the urgent need for modelling tools to conserve our global ecosystem. They can be used to bridge the gap in converting raw data into knowledge to inform sustainable ecosystem management. Increasingly, traditional methods based on Before-After-Control-Impact (BACI) designs and Analysis of Variance (ANOVA) are seen to be unsuitable for ecological data analysis, as they are unable to detect human induced environmental impacts from that of a natural cause. This thesis proves that SOM techniques could be applied to modelling not only a natural systems complexity but also its functioning and dynamics, incorporating spatial as well as temporal variations, to overcome the constraints with conventional methods as applied in other stated disciplines.

The relationship between nutrients and community structure is poorly understood in open-coast habitats. I created a system of artificial tidepools, of identical age and physical dimensions, at two sites that differed in wave exposure, and manipulated nutrient levels and the abundance of herbivores. Using these unique field mesocosms, I explored the role of changes in nutrient dynamics and tested two predictive models of community structure in a rocky intertidal community. I modified a simple food-chain model to include the effect of hydrodynamics on nutrient delivery rates and herbivore foraging efficiency. Field experiments demonstrated that nutrients had strong effects on the abundance and productivity of seaweeds. Algal productivity was negatively influenced by herbivory, contrary to model predictions, because species with the potential to increase growth rates when given additional nutrients were virtually eliminated in the presence of herbivores. The effects of both nutrients and herbivory varied in a manner consistent with predicted effects of hydrodynamic forces. Contrary to simple food-chain models, herbivores did not respond to nutrient additions. I assessed nutrient dynamics during low tide, demonstrating that nutrients were rapidly depleted from tidepools. I also examined variation in nutrient uptake rates relative to the experimental treatments described above, for both whole pools and on a biomass-specific basis. Nutrients were almost always removed from pools at the same rate dispensers added them. Uptake rates were significantly correlated with the abundance of fleshy seaweeds. Synthesizing the results of these and other studies, I proposed that the abundance of tidepool seaweeds can be modeled as a function of pool volume, degree of tidal isolation, water flow at high tide, and herbivory. I tested the predictions of a functional group model and evaluated the validity of equating physical and biological disturbances by examining algal diversity and abundance patterns in tidepools along gradients of potential productivity, herbivory, scour and wave exposure. The abundance of functional groups varied along environmental gradients, but not always in a manner consistent with predictions. I suggested that physical and biological processes must be modeled separately, and that better operational definitions of environmental potentials will aid in development of these models. / Graduation date: 1999

Thesis (Ph.D.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (April 26, 2007) Vita. Includes bibliographical references.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographic references (p.91-111).

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 145-164).

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