| Summary: | Dissertation (Ph.D.) University of Alaska Fairbanks, 2014 The particularly severe effects of climate change anticipated in the Arctic, accompanied by ongoing anthropogenic activities, necessitate proactive and knowledge-based management of the region's aquatic ecosystems. However, the paucity of information on the Arctic's aquatic environments hinders strategic or spatially-explicit management. In this dissertation, I examine the habitat use of poorly studied taxa of the Arctic Coastal Plain (ACP) of Alaska, including freshwater fishes and yellow-billed loons (Gavia adamsii). Distribution studies can be biased by false absences; therefore, I began by determining the detection probabilities of six fish species common to Arctic lakes for five gear types (Chapter 2). Variation in gear- and species-specific detection probability was considerable, suggesting a multi-method approach may be most effective for whole-assemblage sampling. Adjusting for detection probability, I then examine how occupancy probabilities of the six fish species were related to lake and landscape scale covariates (Chapter 3). Three large-bodied salmonid species were influenced by factors associated with the probability of fish colonizing lakes, including whether the lakes had a stream connection. Models for small-bodied fish indicated different strategies for persistence among species. Ninespine stickleback (Pungitius pungitius) were widespread and captured in lakes that freeze to the bottom, suggesting rapid dispersal after spring freshet (when snow and ice had melted rapidly and caused widespread flooding) and colonization of sink habitats. In contrast, Alaska blackfish (Dallia pectoralis) distributions reflect tolerance to harsh conditions, while the slimy sculpin's (Cottus cognatus) was indicative of its marine origin. Based on these patterns, I propose a model of primary controls on the distribution of fishes in ACP lakes. Severe winter conditions limit occupancy through extinction events, while lake occupancy in spring and summer is ...
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