Large‐scale modelling of deep‐diving cetacean habitats

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A Functional Trait Approach to Examine Plant Community Dynamics in South Florida Hardwood Hammock Forests

The tropical hardwood forests of south Florida persist as well-drained patches of broadleaf forest separated by brackish water swamp, marsh, or pineland. In this dissertation, a functional trait approach was used to understand the structure and dynamics of these communities and their responses to abiotic and biotic variation. Twenty-seven permanent plots (20 x 20 m2) were established across the south Florida landscape, representing four sub-regions: Everglades marsh, Long Pine Key, Upper Keys, and Lower Keys. Community weighted mean trait values for four of six selected traits showed significant inter-sub-regional variation. Out of them, three traits (specific leaf area, tree height, and leaf phosphorus) increased significantly from dry and low productivity Florida Keys in the south to the moist and productive areas on the south Florida mainland, while wood density showed the opposite pattern. Trait variance ratios (T-statistic metrics) was used to explore internal filtering (processes that operate within a community) and external filtering (processes that operate at larger scale than that of the individual population or community) on community structure. Both external and internal filtering in the functional composition of south Florida hardwood hammock forest were important for local communities differing in freshwater accessibility, or that occupy different positions along strong edaphic or climatic gradients. To understand the underlying mechanisms that drive species assembly during forest succession in Florida dry sub-tropical forest, 13 leaf, stem, reproductive, and architectural traits of resident tree species across the successional gradient were measured. Tests of null models showed that younger communities are shaped by environmentally driven processes, while mature communities are shaped by competitively driven processes. The overall trait similarities among species present in North Key Largo tropical dry forest suggest that tree species are specialists on the local environment, and their ability to survive and grow in a stressful environment may be more important than competition for resources at larger scale. Moreover, tree species in these forests may exhibit specialization or trait plasticity in coping with drought by changes in their stomatal morphology or activity, allowing for a balance between gas exchange and water loss in a periodically stressful environment. A significant negative correlation between stomatal density and size, and a positive correlation between leaf δ13C and stomatal density were observed across habitat gradient for one of the dominant hardwood hammock species (Bursera simaruba). Small and densely distributed stomates in tandem seems to represent a strategy that allows hammock species to conserve water under physiological drought. Furthermore, findings from this work also showed both intra- and inter-specific trait variation at regional and local scales influence community assembly patterns in hardwood hammock communities in South Florida

Personality, subjective well-being, and the serotonin 1a receptor gene in common marmosets (Callithrix jacchus)

Studies of personality traits in common marmosets (Callithrix jacchus) indicate that there are five or six constructs—Sociability, Dominance, Neuroticism, Openness, and two related to Conscientiousness. The present study attempted to determine whether our earlier study of laboratory-housed individuals only yielded three—Dominance, Sociability, and Neuroticism—because of a low amount of between-subjects variance. To do so, we increased our sample size from 77 to 128. In addition, we ascertained the reliability and validity of ratings and whether polymorphisms related to the serotonin 1a receptor were associated with personality. We found Sociability, Dominance, and Negative Affect factors that resembled three domains found in previous studies, including ours. We also found an Openness and Impulsiveness factor, the latter of which bore some resemblance to Conscientiousness, and two higher-order factors, Pro-sociality and Boldness. In further analyses, we could not exclude the possibility that Pro-sociality and Boldness represented a higher-level of personality organization. Correlations between personality factors and well-being were consistent with the definitions of the factors. There were no significant associations between personality and genotype. These results suggest that common marmoset personality structure varies as a function of rearing or housing variables that have not yet been investigated systematically

Spatially Explicit Population Estimates of the Florida Black Bear

The Florida black bear (Ursus americanus floridanus) is currently comprised of 7 isolated subpopulations: Apalachicola, Eglin, Osceola, Ocala/St. Johns, Chassahowitzka, Highlands/Glades, and Big Cypress. The last statewide assessment of Florida black bear population dynamics was conducted by Simek et al. (2005) using traditional capture-markrecapture methods. The subspecies was removed from Florida’s List of State Threatened Species in 2012 contingent upon the formulation of a management plan that would maintain viable subpopulations of black bears in suitable habitat. Accurate population estimates for each of the remaining black bear subpopulations in Florida were needed to achieve the management goals of this plan. I used spatially explicit capture-recapture (SCR) within a maximum likelihood inference framework to estimate population density (D) and abundance (N) for the Osceola, Ocala/St. Johns, Eglin, Apalachicola, and Big Cypress subpopulations; these 5 subpopulations constitute the bulk of the statewide population. I constructed genetic capture histories for each subpopulation from genotyped hair samples taken from barbed-wire sampling stations (henceforth referred to in this document as hair snares). I used a 3 × 3 hair snare cluster layout with 2 km between hair snares and 16 km between cluster centers. I created covariates of density from land use/land cover (LULC) data to model heterogeneity in density across study areas. Model-averaged population estimates were 120.3 bears (95% CI = 61.1 – 269.1) or 0.025 bears/km2 [bears per square kilometer] (95% CI = 0.013 – 0.056) for the Eglin subpopulation, 1,060.3 bears (95% CI = 825.4 – 1,385.9) or 0.082 bears/km2 (95% CI = 0.064 – 0.107) for Apalachicola, 492.9 bears (95 % CI = 319.5 – 792.4) or 0.127 bears/km2 (95% CI = 0.082 – 0.203) for Osceola, 1,192.6 bears (95% CI = 950.8 – 1,519.5) or 0.127 bears/km2 (95% CI =0.101 – 0.161) for Ocala/St. Johns, and 1,037.4 bears (95% CI = 756.1 – 1,444.6) or 0.131 bears/km2 (95% CI = 0.096 – 0.183) for Big Cypress. Effects of covariates on density estimates varied among study areas. The total population estimate was 3,908.8 bears (95% CI = 2,916.2 – 5,425.8). The cluster sampling method allowed abundance to be estimated across extensive areas that would not have been possible otherwise

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Computational Tools for the Untargeted Assignment of FT-MS Metabolomics Datasets

Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult. A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments. However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments. To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories. These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible

The Dynamics of Human and Rattlesnake Conflict in Southern California

Human-rattlesnake conflict occurs when rattlesnakes are discovered in human-dominated areas and are deemed to pose an unacceptable risk to humans because of their venomous bite. In this dissertation, I investigated the nature of this conflict from the perspectives of both the behavioral and survival risks posed to rattlesnakes and the medical risks posed to humans. In the first of three studies, I investigated the effects of short- and long-distance translocation (SDT and LDT) of nuisance wildlife as a way of mitigating conflict between humans and naturally occurring Red Diamond Rattlesnakes (Crotalus ruber) near residential development in southern California. Snake activity ranges and risk of moving near human-modified areas were larger for LDT and SDT snakes than for non-translocated snakes. Snakes moved closer to human-modified areas and required translocation more often during the summer. Snakes translocated greater distances were less likely to return to human-modified areas, and translocation did not affect snake survival. In the second study, I investigated the etiology and severity of human envenomations using a retrospective review of 354 snakebite cases admitted to Loma Linda University Medical Center between 1990 and 2010. Male snakebite victims and those using alcohol or drugs were more likely to sustain bites to the upper extremity, distal to the ankle or wrist, and via illegitimate provocation of the snake. Snakebite severity was positively associated with snake size, negatively associated with patient mass, and independent of patient age, snake taxon, anatomical location of bite, legitimate versus illegitimate (provoked) bites, and time until hospital admission. Effectiveness of CroFab antivenom was similar for all southern California venomous snake taxa. In the final study, using the same medical data, I assessed the usefulness of several factors as predictors of overall snakebite severity, symptom progression, and antivenom use. Initial snakebite severity score, size of the envenoming snake, and patient mass were significant predictors. I suggested several rules of thumb that could help clinicians anticipate antivenom needs. Overall, this dissertation contributes to our understanding of the effects of mitigation translocation on rattlesnakes and the epidemiology and clinical management of venomous snakebite in southern California

3-D Modelling of Biological Systems for Biomimetics

With the advanced development of computer-based enabling technologies, many engineering, medical, biology, chemistry, physics and food science etc have developed to the unprecedented levels, which lead to many research and development interests in various multi-discipline areas. Among them, biomimetics is one of the most promising and attractive branches of study. Biomimetics is a branch of study that uses biological systems as a model to develop synthetic systems. To learn from nature, one of the fundamental issues is to understand the natural systems such animals, insects, plants and human beings etc. The geometrical characterisation and representation of natural systems is an important fundamental work for biomimetics research. 3D modeling plays a key role in the geometrical characterisation and representation, especially in computer graphical visualization. This chapter firstly presents the typical procedure of 3D modelling methods and then reviews the previous work of 3D geometrical modelling techniques and systems developed for industrial, medical and animation applications. Especially the chapter discusses the problems associated with the existing techniques and systems when they are applied to 3D modelling of biological systems. In addition, the chapter also presents two case studies of authors’ own work. Based upon the discussions, the chapter proposes some areas of research interests in 3D modelling of biological systems and for Biomimetics

Irregular distribution of grid cell firing fields in rats exploring a 3D volumetric space

We investigated how entorhinal grid cells encode volumetric space. On a horizontal surface, grid cells usually produce multiple, spatially focal, approximately circular firing fields that are evenly sized and spaced to form a regular, close-packed, hexagonal array. This spatial regularity has been suggested to underlie navigational computations. In three dimensions, theoretically the equivalent firing pattern would be a regular, hexagonal close packing of evenly sized spherical fields. In the present study, we report that, in rats foraging in a cubic lattice, grid cells maintained normal temporal firing characteristics and produced spatially stable firing fields. However, although most grid fields were ellipsoid, they were sparser, larger, more variably sized and irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid self-organization is shaped by the environment’s structure and/or movement affordances, and grids may not need to be regular to support spatial computations