In south Florida, the Greater Everglades ecosystem supports sixteen species of wading birds. Wading birds serve as important indicator species because they are highly mobile, demonstrate flexible habitat selection, and respond quickly to changes in habitat quality. Models that establish habitat relationships from distribution patterns of wading birds can be used to predict changes in habitat quality that may result from restoration and climate change. I developed spatio-temporal species distribution models for the Great Egret, White Ibis, and Wood Stork over a decadal gradient of environmental conditions to identify factors that link habitat availability to habitat use (i.e., habitat selection), habitat use to species abundance, and species abundance (over multiple scales) to nesting effort and success. Hydrological variables (depth, recession rate, days since drydown, reversal, and hydroperiod) over multiple temporal scales and with existing links to wading bird responses were used as proxies for landscape processes that influence prey availability (i.e., resources). In temporal foraging conditions (TFC) models, species demonstrated conditional preferences for resources based on resource levels at differing temporal scales. Wading bird abundance was highest when prey production from optimal periods of wetland inundation was concentrated in shallow depths. Similar responses were observed in spatial foraging conditions (SFC) models predicting spatial occurrence over time, accounting for spatial autocorrelation. The TFC index represents conditions within suitable depths that change daily and reflects patch quality, whereas the SFC index spatially represents suitability of all cells and reflects daily landscape patch abundance. I linked these indices to responses at the nest initiation and nest provisioning breeding phases from 1993-2013. The timing of increases and overall magnitude of resource pulses predicted by the TFC in March and April were strongly linked to breeding responses by all species. Great Egret nesting effort and success were higher with increases in conspecific attraction (i.e., clustering). Wood Stork nesting effort was closely related to timing of concurrently high levels of patch quality (regional scale) and abundance (400-m scale), indicating the importance of a multi-scaled approach. The models helped identify positive and negative changes to multi-annual resource pulses from hydrological restoration and climate change scenarios, respectively. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at Everglades National Park, FL, USA. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection