The St. Croix ground lizard, Ameiva polops, is a United States endangered species endemic to St. Croix, U.S. Virgin Islands. It was extirpated from St. Croix Proper by invasive mongooses, and remaining populations are on small, nearby cays. In the summer of 2008, as part of the recovery plan for this species, I worked in a multi-agency effort to translocate a population of A. polops to Buck Island Reef National Monument, U.S. Virgin Islands to focus on two main objectives: 1) examine the detection probability of A. polops and infer the consequences of it on population estimates; and 2) examine whether A. polops may deplete its prey base or alter the arthropod assemblage at the translocation site. We used a soft-release strategy for the translocation, in which 57 lizards were initially contained in a series of eight 10 m x 10 m enclosures in the habitat on Buck Island for monitoring. As part of the initial monitoring I conducted visual surveys through all enclosures, with the known number of lizards, to calculate the detection probability and to demonstrate how many individuals would be estimated using visual encounter surveys of this known population. Adjacent to enclosures housing A. polops were control enclosures, without A. polops, which I used to test whether the translocated lizards would impact their prey base over 6 weeks. I found that the detection probability of A. polops is very low (<0.25), which causes population sizes to be severely underestimated, even using some mark-resight techniques. My study of A. polops on the prey community indicated that the lizards generally had no effect on abundance or diversity of arthropods in general, though they may cause small changes for particular taxa. My results help corroborate other evidence that accuracy of population enumeration techniques needs to be improved in order to adequately understand the status of wildlife populations. Additionally, prey resources do not seem to be limiting A. polops in the short-term, and I expect the population will grow, expanding through Buck Island. Future monitoring will be carried out by the National Park Service using robust mark-resight techniques.

I focus on phylogenetic relationships of teiid lizards beginning with generic and species relationship within the family, followed by a detailed biogeographical examination of the Caribbean genus Pholidoscelis, and end by studying species boundaries and phylogeographic patterns of the widespread Giant Ameiva Ameiva ameiva. Genomic data (488,656 bp of aligned nuclear DNA) recovered a well-supported phylogeny for Teiidae, showing monophyly for 18 genera including those recently described using morphology and smaller molecular datasets. All three methods of phylogenetic estimation (two species tree, one concatenation) recovered identical topologies except for some relationships within the subfamily Tupinambinae (i.e. position of Salvator and Dracaena) and species relationships within Pholidoscelis, but these were unsupported in all analyses. Phylogenetic reconstruction focused on Caribbean Pholidoscelis recovered novel relationships not reported in previous studies that were based on significantly smaller datasets. Using fossil data, I improve upon divergence time estimates and hypotheses for the biogeographic history of the genus. It is proposed that Pholidoscelis colonized the Caribbean islands through the Lesser Antilles based on biogeographic analysis, the directionality of ocean currents, and evidence that most Caribbean taxa originally colonized from South America. Genetic relationships among populations within the Ameiva ameiva species complex have been poorly understood as a result of its continental-scale distribution and an absence of molecular data for the group. Mitochondrial ND2 data for 357 samples from 233 localities show that A. ameiva may consist of up to six species, with pairwise genetic distances among these six groups ranging from 4.7–12.8%. An examination of morphological characters supports the molecular findings with prediction accuracy of the six clades reaching 72.5% using the seven most diagnostic predictors.