Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

Modeling with Time Gaps : Application to UW-Eau Claire Housing Incidents

Color poster with text and graphs.The purpose of this project is to develop a framework for time series analysis on data sets which have long known periods of missing information. Such a situation presents itself when trying to predict judicial code violations in the UW-Eau Claire residence halls during the fall and spring semesters. This project presents a way to predict the number of students involved in these violations based on past observations and future known conditions. The effects of a full moon, homecoming, finals week, weekends, and semester breaks on the prevalence of students involved are considered and quantified. Time lags accounting for weekly and daily auto-correlation as well as a yearly moving average are incorporated. Because data is unavailable for summer and winter sessions, special consideration is made in the model for the beginnings of semesters. Better understanding the patterns of on-campus incidents over time will contribute meaningful insights for hall directors, resident assistants, and entities who support and protect students. In addition, this study can inform disciplines like sports analytics as well as other areas in which large time gaps might also be encountered when implementing time series analyses.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Baseball Analytics : Further Modeling

Color poster with text, images, charts, and graphs.Baseball has long served as a frontrunner for the integration of analytics with application, connected in part to the consistency of historical statistics as well as the widespread interest in the national professional and more local semi-professional sport leagues. This project builds on prior data-exploration and data-gathering for the local Northwoods League, with recognition of a reduced set of available technical information (as compared to MLB). The intention is to create a WAR calculation for the league using the available data. The original WAR statistic in the MLB uses data that is unattainable for the NWL. Therefore, this project attempts to modify the WAR statistic with reasonable substitutes to make it accessible for the NWL. The two primary focuses of the current project are modeling and coding generalization. Coding generalization summarizes the structure of the available data organization as well as discusses functions written to compute necessary inputs for the metrics, including different portions of the WAR analogy. With the adjustments and additional back-computations of metrics for individual players, we now have sufficient available information for modeling these player utilities. We summarize the modeling results found by connecting information about: player-by-game appearance, season-break information, refined player utility estimates, and use of reconfigured statistics.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Tracking COVID Locally and Adaptively

Color poster with text, charts, and graphs.In summer 2020, the project started with the faculty mentor created a dashboard to visualize and summarize information about local COVID data. Skills developed include learning a new programing package dplyr in R and hosting code on GitHub, which were then applied in preparatory work such as building new data frames and calculations. Thus, we will discuss a predictive time series model with lagged counts for future outcomes (such as hospitalizations), built on age-grouped case-counts to account for the disparities in outcomes observed for different ages in the COVID pandemic.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Lunar Effects on UW-Eau Claire On-Campus Housing Incidents

Color poster with text, charts, and graphs.The relationship between lunar phases and human behavior has been a topic of folklore for centuries. In the last century, it has been studied scientifically with varying results. In this study, we analyzed the effect of the full moon on incidents reported in UWEC housing. We used Non-parametric methods, as well as Time Series and Generalized Linear Models.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Analytics for Local Collegiate Baseball League : Improved Statistics and Favorable Factors

Color poster with text, charts, and graphs.This project focuses on analytics methods based on traditional, historic statistics gathered for baseball players, as well as team win-loss records within a defined competitor framework. Methodologies for both team-level and player-level analyses were adjusted for the Northwoods League, including the local team Eau Claire Express, using historical data. We hope to be able to provide value to the local community by sharing some of the insights gained. Assessments of individual player batting and pitching strengths were computed, based on statistics developed recently within Major League Baseball; explanation of these metrics is available on sites such as at FanGraphs. Comparisons of these newer metrics are made to historical assessment measures. Summaries of team records were gathered across the most recent four seasons, for 18-20 teams in the league. Various recursive record-updating methods were considered for predictive purposes. The current analysis examines summary statistic values that appear to be most associated with streaks of wins or losses. Methods for modeling streaks by incorporating team statistics and other metrics are examined. This outline to the right tracks the research, data management, and programming used to gather, clean, compute, organize, and evaluate the team and individual player data for the Northwoods League.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Analytics for Summer Collegiate Baseball : Connecting Individual and Team Results

Color poster with text, images, charts, and graphs.A prior study on baseball analytics for the Northwoods League summarized the available components needed to calculate a "Win Above Replacement" (WAR) metric for individual players, as well as identified missing and potential replacement measures in this league. Some of the inputs were found to be unobtainable due to lack of technical equipment in the league, but research into the origins of the measure and historic records allowed us to identify some suitable substitute measures. Obtaining these measures is possible due solely to numerous programming and data-gleaning achievements. These include code to pull and summarize play-by-play information from the original online text source; back-computing the physical locations of defensive plays from an image of the playing field; and creating a system both to extract and to compute player and team metrics beyond those automatically provided for the Northwoods League data. Summary of these methods will be included in the presentation, along with a discussion of the structure of the usable data, including play-by-play output, individual-player, and league-level summaries. Additionally, progress towards a WAR-analogy estimation (using a model connecting team performance and player appearance) will be included.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

Quantitative Structure-activity Relationship Modeling of Mosquito Repellents Using Calculated Descriptors

Quantitative structure-activity relationship (QSAR) modeling of 40 DEET-related mosquito repellents was carried out using calculated molecular descriptors. When the four different classes of descriptors (topochemical, topostructural, geometrical and quantum chemical) were used in a hierarchical fashion, topochemical descriptors were found to explain much of the variance in the data and this indicated the importance of the chemical nature of the atoms and groups towards repellency of these compounds. Ridge regression (RR) outperformed partial least square regression (PLS) and principal component regression (PCR). We also used descriptor thinning via a modified Gram-Schmidt algorithm prior to ridge regression. This resulted in a four-parameter model with a 20-fold cross-validated R2 of 0.734. The q2 (R2cv) reported here is the "true-q2" because the descriptor thinning was embedded within the cross-validation step. Inclusion of any calculated physicochemical property (secondary descriptor) did not result in improvement of the models built with the calculated molecular descriptors (primary descriptors). This result has great implications in the QSAR assisted design of new mosquito repellents because calculation of the primary descriptors does not require any input other than the molecular structure