Traits, Species, and Communities: Integrative Bayesian Approaches to Ecological Biogeography across Geographic, Environmental, Phylogenetic, and Morphological Space

• Other Authors: Humphreys, John M. (author)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Geography; Biology; Statistics
• Online Access: http://purl.flvc.org/fsu/fd/2018_Sp_Humphreys_fsu_0071E_14298

Assuming a methodological perspective, this dissertation proceeds through a series of studies that cover levels of biological organization ranging from the morphological traits of individual specimens to community assemblages. The presented research explores geographic extents ranging from local to global scales, examines both plants and animals, and explores relationships among species with common ancestry. The research appraises and then proposes solutions to a variety of yet unresolved issues in species distribution modeling; including, preferential sampling, spatial dependency, multi-scaled spatial processes, niche equilibrium assumptions, data structure arising from shared evolutionary history, and correlations between predictor variables. Approaching the geographic distribution of wetlands as an applied concern, the study presented in Chapter 2 emphasizes that the identication and inventory of wetlands are essential components of water resource management. To be eective in these endeavors, it is critical that the process used to detect and document wetlands be time ecient, accurate, and repeatable as new environmental information becomes available. Approaches dependent on aerial photographic interpretation of land cover by individual human analysts necessitate hours of assessment, introduce human error, and fail to include the best available soils and hydrologic data. The goal of Chapter 2 is to apply hierarchical modeling and Bayesian inference to predict the probability of wetland presence as a continuous gradient with the explicit consideration of spatial structure. The presented spatial statistical model can evaluate 100 km2 at a 50 x 50 meter resolution in approximately 50 minutes while simultaneously incorporating ancillary data and accounting for latent spatial processes. Model results demonstrate an ability to consistently capture wetlands identied through aerial interpretation with greater than 90% accuracy (scaled Brier Score) and to identify wetland extents, ecotones, and hydrologic connections not identied through use of other modeling and mapping techniques. The provided model is reasonably robust to changes in resolution, areal extents between 100 km2 and 300 km2, and region-specic physical conditions. As with modeling wetland occurrence, species distribution modeling aimed at forecasting the spread of invasive species under projected global warming also oers land managers an important tool for assessing future ecological risk and for prioritizing management actions. Chapter 3 applies Bayesian inference and newly available geostatistical tools to forecast global range expansion for the ecosystem altering invasive climbing fern Lygodium microphyllum. The presented modeling framework emphasizes the need to account for spatial processes at both the individual and aggregate levels, the necessity of modeling non-linear responses to environmental gradients, and the explanatory power of biotic covariates. Results indicate that Lygodium microphyllum will undergo global range expansion in concert with anthropogenic global warming and that the species is likely temperature and dispersal limited. Predictions are presented for current and future climate conditions assuming both limited and unlimited dispersal scenarios. Finally, Chapter 4 provides a novel framework to combine multi-species joint modeling techniques with spatially explicit phylogenetic regression to simultaneously predict the probability of species occurrence and the geographic distribution of interspecic continuous morphological traits. Choosing the South American leaf-eared mice (genus: Phyllotis) as an empirical example, a threetiered phylogenetic coregionalization trait biogeography model (PhyCoRTBio) is constructed. The conditionally dependent structure of the PhyCoRTBio model enables information from multiple species and from multiple specimen-specic trait metrics to be leveraged towards estimation of a focal species distribution. I hypothesize that, relative to other commonly used species distribution modeling methods, the PhyCoRTBio approach will exhibit improved performance in predicting occurrence for species within the genus Phyllotis. After describing its statistical implementation, this hypothesis is assessed by constructing PhyCoRTBio models for six dierent Phyllotis species and then comparing results to those derived using maximum entropy methods, random forest clustering, Gaussian random eld species distribution models, and Hierarchical Bayesian species distribution models. To judge the relative performance of each modeling approach, model sensitivity (proportion of correctly predicted presences), specicity (proportion of correctly predicted absences), the area under the receiver operating characteristic curve (AUC), and the True Skill Statistic (TSS) are calculated. Findings indicate that trait-based covariates improve model performance and highlight the need to consider spatial processes and phylogenetic information during multi-species distribution modeling. === A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Spring Semester 2018. === February 23, 2018. === Bayesian, Biogeography, Phyllotis, Phylogenetic, Spatial Statistics, Wetland === Includes bibliographical references. === James B. Elsner, Professor Directing Dissertation; Scott J. Steppan, University Representative; Victor Mesev, Committee Member; Stephanie Pau, Committee Member.