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USING LANDSCAPE GENETICS TO ASSESS POPULATION CONNECTIVITY IN A HABITAT GENERALIST
Title
USING LANDSCAPE GENETICS TO ASSESS POPULATION CONNECTIVITY IN A HABITAT GENERALIST.
Creator
Hether, Tyler, Hoffman, Eric, University of Central Florida
Abstract / Description
Understanding the nature of genetic variation in natural populations is an underlying theme of population genetics. In recent years population genetics has benefited from the incorporation of landscape and environmental data into pre-existing models of isolation by distance (IBD) to elucidate features influencing spatial genetic variation. Many of these landscape genetics studies have focused on populations separated by discrete barriers (e.g., mountain ridges) or species with specific...
Show moreUnderstanding the nature of genetic variation in natural populations is an underlying theme of population genetics. In recent years population genetics has benefited from the incorporation of landscape and environmental data into pre-existing models of isolation by distance (IBD) to elucidate features influencing spatial genetic variation. Many of these landscape genetics studies have focused on populations separated by discrete barriers (e.g., mountain ridges) or species with specific habitat requirements (i.e., habitat specialists). One difficulty in using a landscape genetics approach for taxa with less stringent habitat requirements (i.e., generalists) is the lack of obvious barriers to gene flow and preference for specific habitats. My study attempts to fill this information gap to understand mechanisms underlying population subdivision in generalists, using the squirrel treefrog (Hyla squirella) and a system for classifying 'terrestrial ecological systems' (i.e. habitat types). I evaluate this dataset with microsatellite markers and a recently introduced method based on ensemble learning (Random Forest) to identify whether spatial distance, habitat types, or both have influenced genetic connectivity among 20 H. squirella populations. Next, I hierarchically subset the populations included in the analysis based on (1) genetic assignment tests and (2) Mantel correlograms to determine the relative role of spatial distance in shaping landscape genetic patterns. Assignment tests show evidence of two genetic clusters that separate populations in Florida's panhandle (Western cluster) from those in peninsular Florida and southern Georgia (Eastern cluster). Mantel correlograms suggest a patch size of approximately 150 km. Landscape genetic analyses at all three spatial scales yielded improved model fit relative to isolation by distance when including habitat types. A hierarchical effect was identified whereby the importance of spatial distance (km) was the strongest predictor of patterns of genetic differentiation above the scale of the genetic patch. Below the genetic patch, spatial distance was still an explanatory variable but was only approximately 30% as relevant as mesic flatwoods or upland oak hammocks. Thus, it appears that habitat types largely influence patterns of population genetic connectivity at local scales but the signal of IBD becomes the dominant driver of regional connectivity. My results highlight some habitats as highly relevant to increased genetic connectivity at all spatial scales (e.g., upland oak hammocks) while others show no association (e.g., silviculture) or scale specific associations (e.g., pastures only at global scales). Given these results it appears that treating habitat as a binary metric (suitable/non-suitable) may be overly simplistic for generalist species in which gene flow probably occurs in a spectrum of habitat suitability. The overall pattern of spatial genetic and landscape genetic structure identified here provides insight into the evolutionary history and patterns of population connectivity for H. squirella and improves our understanding of the role of matrix composition for habitat generalists.
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Date Issued
2010
Identifier
CFE0003204, ucf:48580
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003204
GENETIC AND PHENOTYPIC EVOLUTION IN THE ORNATE CHORUS FROG (PSEUDACRIS ORNATA): TESTING THE RELATIVE ROLES OF NATURAL SELECTION, MIGRATION, AND GENETIC DRIFT
Title
GENETIC AND PHENOTYPIC EVOLUTION IN THE ORNATE CHORUS FROG (PSEUDACRIS ORNATA): TESTING THE RELATIVE ROLES OF NATURAL SELECTION, MIGRATION, AND GENETIC DRIFT.
Creator
Degner, Jacob, Hoffman, Eric, University of Central Florida
Abstract / Description
Understanding how migration, genetic drift, and natural selection interact to maintain the genetic and phenotypic variation we observe in natural populations is a central goal of population genetics. Amphibians provide excellent model organisms for investigating the interplay between these evolutionary forces because amphibians are generally characterized by limited dispersal abilities, high philopatry, and are obligately associated with the areas around suitable habitats (e.g. breeding ponds...
Show moreUnderstanding how migration, genetic drift, and natural selection interact to maintain the genetic and phenotypic variation we observe in natural populations is a central goal of population genetics. Amphibians provide excellent model organisms for investigating the interplay between these evolutionary forces because amphibians are generally characterized by limited dispersal abilities, high philopatry, and are obligately associated with the areas around suitable habitats (e.g. breeding ponds). Thus, on relatively small geographic scales, the relative effects of all of these evolutionary forces can be studied together. Here, we study the interaction of migration, genetic drift, natural selection, and historical process in the ornate chorus frog (Pseudacris ornata). We report the development and characterization of 10 polymorphic microsatellite genetic markers. Number of alleles per locus ranged from 2 to 21 averaging 9.2 and expected heterozygosities ranged from 0.10 to 0.97 averaging 0.52. However, in an analysis of two populations, three locus-by-population comparisons exhibited significant heterozygote deficiencies and indicated that null alleles may be present some loci. Furthermore, we characterized genetic structure and historical biogeographic patterns in P. ornata using these microsatellite markers along with mitochondrial DNA sequence data. Our data indicate that in these frogs, migration may play a large role in determining population structure as pairwise estimates of FST were relatively small ranging from 0.04 to 0.12 (global FST = 0.083). Additionally, we observed an overall pattern of isolation-by-distance in neutral genetic markers across the species range. Moreover, our data suggest that the Apalachicola River basin does not impede gene flow in P. ornata as it does in many vertebrate taxa. Interestingly, we identified significant genetic structure between populations separated by only 6 km. However, this fine scale genetic structure was only present in the more urbanized of two widespread sampling localities. Finally, in this study, we demonstrated that there was a significant correlation between the frequency of green frogs and latitude. There was a higher frequency of green frogs in southern samples and a lower frequency of green frogs in northern samples. However, when we interpreted this phenotypic cline in light of the overall pattern of isolation-by-distance, it was apparent that the neutral evolutionary forces of genetic drift and migration could explain the cline, and the invocation of natural selection was not necessary.
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Date Issued
2007
Identifier
CFE0001721, ucf:47319
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001721
Optimal distribution network reconfiguration using meta-heuristic algorithms
Title
Optimal distribution network reconfiguration using meta-heuristic algorithms.
Creator
Asrari, Arash, Wu, Thomas, Lotfifard, Saeed, Haralambous, Michael, Atia, George, Pazour, Jennifer, University of Central Florida
Abstract / Description
Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the...
Show moreFinding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network.The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search.
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Date Issued
2015
Identifier
CFE0005575, ucf:50238
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005575