Insects are able to navigate their environments because they can detect hydrocarbons and volatile odors, but it is not clear which one has the fastest reaction when detected, or how much of a response can be produced due to either one. In order to determine which category of odorant is detected first as well as which one causes the highest response rate, data on electrophysiological responses from ants was analyzed. While the statistical tests can be done to understand and answer the questions raised by the study, there are various hydrocarbons and volatile odors that were not used in the data. Conclusive evidence only applies to the odorants used in the experiments.
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
Although social hierarchies are commonly found all throughout nature, the underlying mechanisms of their formation are still ambiguous. Hierarchies form through a wide range of interactions between subordinate and dominant individuals, and the ponerine ant Harpegnathos saltator provides the perfect model to explore such dominance behaviors. When the queen is absent or her fecundity levels drop below a certain threshold, H. saltator workers undergo a dominance tournament, in which several individuals emerge as gamergates, reproductive workers that are not queens. During this tournament, several characterizable dominance behaviors are exhibited (antennal dueling, dominance biting, and policing), which can be used to study the behavioral and social dynamics in the formation of a reproductive hierarchy. Colonies of 15, 30, 60, and 120 workers were created in duplicate, and their dominance tournaments were recorded to study how these interactions impact gamergate establishment. Rather than studying these behaviors as isolated incidents, responses to policing behaviors (timid, neutral, or aggressive) and their duration were recorded along with the frequency of dueling. Three groups were determined: dueling future gamergates (DFG), dueling future non-gamergates (DFNG) and non-dueling individuals (ND). DFNG received many more policing attacks and the duration of these interactions lasted much longer. DFG consistently exhibited the most dueling. Timid and neutral responses were more common than aggressive responses, perhaps due to energy conversation purposes. Peaks in dueling correspond to peaks in policing, highlighting the dynamic behavioral interactions necessary for the formation of a reproductive hierarchy.
technological systems that affect society, such as communication infrastructures. Data
assimilation addresses the challenge of state specification by incorporating system
observations into the model estimates. In this research, a particular data
assimilation technique called the Local Ensemble Transform Kalman Filter (LETKF) is
applied to the ionosphere, which is a domain of practical interest due to its effects
on infrastructures that depend on satellite communication and remote sensing. This
dissertation consists of three main studies that propose strategies to improve space-
weather specification during ionospheric extreme events, but are generally applicable
to Earth-system models:
Topic I applies the LETKF to estimate ion density with an idealized model of
the ionosphere, given noisy synthetic observations of varying sparsity. Results show
that the LETKF yields accurate estimates of the ion density field and unobserved
components of neutral winds even when the observation density is spatially sparse
(2% of grid points) and there is large levels (40%) of Gaussian observation noise.
Topic II proposes a targeted observing strategy for data assimilation, which uses
the influence matrix diagnostic to target errors in chosen state variables. This
strategy is applied in observing system experiments, in which synthetic electron density
observations are assimilated with the LETKF into the Thermosphere-Ionosphere-
Electrodynamics Global Circulation Model (TIEGCM) during a geomagnetic storm.
Results show that assimilating targeted electron density observations yields on
average about 60%–80% reduction in electron density error within a 600 km radius of
the observed location, compared to 15% reduction obtained with randomly placed
vertical profiles.
Topic III proposes a methodology to account for systematic model bias arising
ifrom errors in parametrized solar and magnetospheric inputs. This strategy is ap-
plied with the TIEGCM during a geomagnetic storm, and is used to estimate the
spatiotemporal variations of bias in electron density predictions during the
transitionary phases of the geomagnetic storm. Results show that this strategy reduces
error in 1-hour predictions of electron density by about 35% and 30% in polar regions
during the main and relaxation phases of the geomagnetic storm, respectively.