Linking herbivory and pollination: Costs and selection implications in Centrosema virginianum Bentham (Fabaceae: Papilionoideae)

This research first evaluated levels and type of herbivory experienced by Centrosema virginianum plants in their native habitat and how florivory affected the pollinator activity. I found that populations of C. virginianum in two pine rockland habitat fragments experienced higher herbivory levels (15% and 22%) compared with plants in the protected study site (8.6%). I found that bees (Hymenoptera) pollinated butterfly pea. Furthermore, I found that florivores had a negative effect in the pollinators visitation rates and therefore in the seed set of the population. ^ I then conducted a study using a greenhouse population of C. virginianum. I applied artificial herbivory treatments: control, mild herbivory and severe herbivory. Flower size, pollen produced, ovules produced and seeds produced were negatively affected by herbivory. I did not find difference in nectar volume and quality by flowers among treatments. Surprisingly, severely damaged plants produced flowers with larger pollen than those from mildly damaged and undamaged plants. Results showed that plants tolerated mild and severe herbivory with 6% and 17% reduction of total fitness components, respectively. However, the investment of resources was not equisexual. ^ A comparison in the ability of siring seeds between large and small pollen was necessary to establish the biological consequence of size in pollen performance. I found that fruits produced an average of 18.7 ± 1.52 and 17.7 ± 1.50 from large and small pollen fertilization respectively. These findings supported a pollen number-size trade-off in plants under severe herbivory treatments. As far as I know, this result has not previously been reported. ^ Lastly, I tested how herbivory influenced seed abortion patterns in plants, examining how resources are allocated on different regions within fruits under artificial herbivory treatments. I found that self-fertilized fruits had greater seed abortion rates than cross-fertilized fruits. The proportion of seeds aborted was lower in the middle regions of the fruits in cross-fertilized fruits, producing more vigorous progeny. Self-fertilized fruits did not show patterns of seedling vigor. I also found that early abortion was higher closer to the peduncular end of the fruits. Position of seeds within fruits could be important in the seed dispersion mechanism characteristic of this species. ^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.