Herbivory is a key process that mediates the abundance of primary producers and community composition in both terrestrial and aquatic ecosystems. On tropical coral reefs, changes in herbivory are often related to phase shifts between coral-dominance and dominance by seaweeds, or foliose macroalgae. Resilience or capacity to resist and reverse such phase shifts is, therefore, viewed as a critical function on coral reefs. This thesis used grazer exclusion and assay experiments at six sites within three different fisheries management regimes in Kenya to identify the impacts of herbivores (sea urchins and fishes) on algal dynamics in the context of coral reef resilience. First, I examined the grazing rates necessary to prevent phase shifts by quantifying consumption and algal production. Here, I found that, over a 390-day experiment, at least 50 percent of algal production must be consumed to avoid accumulation of algal biomass. Using video observations, I also showed that scraping parrotfishes remove more algae (per unit of fish biomass) than previously assumed, and that sea urchins, if released from predation, have similar impacts to fishes. Then I focused on algal succession, and found that sea urchins and fishes have different effects that are mediated by their abundances and species composition. Where sea urchins were less abundant and parrotfishes absent (e.g. young fisheries closures), progression of algae from turfs to early and then late successional macroalgae occurred rapidly and within 100 days. I then turned my focus to the removal of already established macroalgae (grown for > 1 yr in the absence of herbivores) and showed that sea urchins and browsing fishes were able to remove significant amounts of macroalgae where either herbivore was abundant. However, using multiple-choice selectivity assays and in situ video recordings, I found that browsing fishes fed very selectively with low overlap in diet among species, leading to low functional redundancy within a high diversity system. Finally, using long-term survey data (from 28 sites) to build a 43-year chronosequence, I showed that it is possible that the effects of herbivory will not be constant across transitions from open fishing to fishery closures through non-linear grazing intensity. Therefore, increases in herbivory within fisheries closures may not be immediate and may allow a window of opportunity for algae to go from turf to unpalatable macroalgae until scraping and browsing fishes fully recover from fishing (~ 20 years). The findings in this thesis are novel and raise concern over the potential implications of the slow recovery of parrotfishes or, given lower than expected functional redundancy in grazing effects, the absence of even one browsing fish species in fisheries closures. Overall, this thesis highlights the importance of herbivore community dynamics in mediating interactions among algae, and provides new insights for conservation and management actions that attempt to bolster the resilience of coral reefs.

Indiana University-Purdue University Indianapolis (IUPUI) / In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased Fuzzy Inference System Models (ANFIS) were developed and implemented to determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for understanding algae dynamics and developing prediction methods for algal taste and odor release events. In this research the methodology for prediction of 2-MIB and geosmin production was explored. The approach incorporated a combination of numerical and heuristic modeling to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s variable data measured at monitoring stations and consisting of chemical/physical and biological parameters with the addition of calculated mixing conditions within the reservoir were used to train and validate the models. The Adaptive – Network based Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of multiple model constraints. The predictions followed the generally observed trends of algal metabolites during the three seasons over three years (2008-2010). The randomly selected data pairs for geosmin for validation achieved coefficient of determination of 0.78, while 2-MIB validation was not accepted due to large differences between two observations and their model prediction. Although, these ANFIS results were accepted, the further application of the ANFIS model coupled with the numerical models to predict spatio-temporal distribution of metabolites showed serious limitations, due to numerical model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp. biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave negative biovolumes, which were assumed to represent zero values observed at the station. The taste and odor metabolite, geosmin, was under-predicted as the predicted v concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB model did not validate during EFDC to ANFIS model evaluation. The proposed approach and developed methodology could be used for future applications if the limitations are appropriately addressed.