Biodiversity inventories are a critical resource, providing baseline information for assessing

environmental changes over time. In many cases, the underlying datasets are generated by

"opportunistic" sampling efforts or they are consolidated from diverse datasets collected for different purposes. These datasets are typically patchy and incomplete, requiring the use of sophisticated statistical analyses. The Antarctic Peninsula (AP) is one of those areas where direct observation of species distribution is difficult; it is also an area that in recent decades has been experiencing important environmental changes, which influence population and ecosystem dynamics. I addressed biogeographical questions in the AP archipelago, using remote sensing and opportunistic data sets for two very different groups of organisms: lichens and penguins. Although taxonomically different, both groups are key components of the AP terrestrial ecosystem, and share the need to couple biodiversity surveys with modeling to understand species distribution and abundance patterns in large areas of remote wilderness. The results of this dissertation work are interesting to polar biologists, because evidence suggests that the input of nutrients by seabirds can significantly impact floral diversity and abundance in nutrient-poor polar communities. The datasets and protocols for data collection and analyses generated in this project are valuable in themselves for the scientific community. They could be used as the basis for a valuable and practicable monitoring program and procedures for the evaluation of the data derived from it. In the Antarctic Peninsula in particular, this information will aid in the delineation and management of protected areas, as well as in the evaluation of the impacts of climate change and human visitation to the most traveled locations. Furthermore, this research provided an example of how an approach that integrates the use of existing remote-sensing products with independent ongoing field sampling efforts, "citizen scientist" data collection, and historical datasets can yield low-cost, high-benefit studies that can be useful both to understand how species respond to their environment, and to help environmental managers to predict and cope with imminent changes due to global warming.