The Induced Heart Rate Response to Fish Kairomones in \u3ci\u3eDaphnia pulex\u3c/i\u3e

Daphnia pulex have been shown to respond to kairomones emitted by predatory Chaoborus and planktivorous fish, though these two groups of predators differ significantly in their predation styles. The effects of predation vary across Daphnia size range, and heart rate response to Chaoborus kairomones has been shown to vary across size. I found that heart rate of individuals in the small size class in a clonal population of Daphnia pulex respond more strongly to bluegill (Lepomismacrochirus) kairomones than heart rate of medium or large size classes. The two largest classes showed no difference in heart rate between control and fish kairomone treatments. This is possibly due to physiological differences between small and large Daphnia pulex, or it could be an adaptive response based on the futility of escape from fish predation for large Daphnia and the lower detection rates for small Daphnia

Agent-based and continuous models of hopper bands for the Australian plague locust: How resource consumption mediates pulse formation and geometry

Locusts are significant agricultural pests. Under favorable environmental conditions flightless juveniles may aggregate into coherent, aligned swarms referred to as hopper bands. These bands are often observed as a propagating wave having a dense front with rapidly decreasing density in the wake. A tantalizing and common observation is that these fronts slow and steepen in the presence of green vegetation. This suggests the collective motion of the band is mediated by resource consumption. Our goal is to model and quantify this effect. We focus on the Australian plague locust, for which excellent field and experimental data is available. Exploiting the alignment of locusts in hopper bands, we concentrate solely on the density variation perpendicular to the front. We develop two models in tandem; an agent-based model that tracks the position of individuals and a partial differential equation model that describes locust density. In both these models, locust are either stationary (and feeding) or moving. Resources decrease with feeding. The rate at which locusts transition between moving and stationary (and vice versa) is enhanced (diminished) by resource abundance. This effect proves essential to the formation, shape, and speed of locust hopper bands in our models. From the biological literature we estimate ranges for the ten input parameters of our models. Sobol sensitivity analysis yields insight into how the band's collective characteristics vary with changes in the input parameters. By examining 4.4 million parameter combinations, we identify biologically consistent parameters that reproduce field observations. We thus demonstrate that resource-dependent behavior can explain the density distribution observed in locust hopper bands. This work suggests that feeding behaviors should be an intrinsic part of future modeling efforts.Comment: 26 pages, 11 figures, 3 tables, 3 appendices with 1 figure; revised Introduction, Sec 1.1, and Discussion; cosmetic changes to figures; fixed typos and made clarifications throughout; results unchange

Bugs Behaving Badly: Insect Pest Behavior and Pathogen-Induced Cannibalism

The study of insect behavior in agroecosystems offers opportunities to understand fundamental concepts in behavioral ecology and to improve the management of pests for the sake of agricultural production. The following dissertation is a series of papers focused on insect pest behavior and trait-mediated effects. Chapter 1 is a synthetic review paper that develops a framework for utilizing the large body of literature on enemy-risk effects to improve arthropod pest management. While the field of biological control of pests is built on natural enemy ecology, insights gleaned from the study of enemy-risk effects have not been fully incorporated into biological control practice. This paper provides an overview of key concepts from both fields, reviews the literature where they intersect, and provides both conceptual discussions and case studies of particularly relevant applications. Chapter 2 is a paper modeling the collective movement behavior of a devastating pest, the Australian plague locust, using combined agent- based and partial differential equation models. While many models of locust movement rely on interactions between individuals, we demonstrate that foraging behavior generates the characteristic traveling wave pattern of Australian plague locust hopper bands. Finally, Chapter 3 is a manuscript investigating an agent-based model of the population and disease dynamics of a system where infection induces cannibalistic behavior. This study was inspired in part by the case of Geocoris pallens, a common beneficial insect in agricultural systems, whose California populations have suffered from increased cannibalism in the face of a novel pathogen. While only empirically documented in a few systems, disease often induces energetic stress, a key trigger of cannibalistic behavior, which is widespread among animal taxa. I present the results of a series of in silico experiments investigating the interacting effects of pathogenic virulence and pathogen-induced cannibalism, showing that the combination can lead to drastic suppression of host populations and even host extinction

Quantifying critical N dilution curves across G × E × M effects for potato using a partially-pooled Bayesian hierarchical method

Multiple critical N dilution curves [CNDCs] have been previously developed for potato; however, attempts to directly compare differences in CNDCs across genotype [G], environment [E], and management [M] interactions have been confounded by non-uniform statistical methods, biased experimental data, and lack of proper quantification of uncertainty in the critical N concentration [%Nc]. This study implements a partially-pooled Bayesian hierarchical method to develop CNDCs for previously published and newly reported experimental data, systematically evaluates the difference in %Nc [∆%Nc] across G × E × M effects, and directly compare CNDCs from the Bayesian framework to CNDCs from conventional statistical methods. The partially-pooled Bayesian hierarchical method implemented in this study has the advantage of being less susceptible to inferential bias at the level of individual G × E × M interactions compared to alternative statistical methods that result from insufficient quantity and quality of experimental datasets (e.g., unbalanced distribution of N limiting and non-N limiting observations). This method also allows for a direct statistical comparison of differences in %Nc across levels of the G × E × M interactions. Where found to be significant, ∆%Nc was hypothesized to be related to variation in the timing of tuber initiation (e.g., maturity class) and the relative rate of tuber bulking (e.g., planting density) across G x E × M interactions. In addition to using the median value for %Nc (i.e., CNDC), the lower and upper boundary values for the credible region (i.e., CNDClo and CNDCup) derived using the Bayesian framework should be used in calculation of N nutrition index (and other calculations) to account for uncertainty in %Nc. Overall, this study provides additional evidence that%Nc is dependent upon G × E × M interactions; therefore, evaluation of crop N status or N use efficiency must account for variation in %Nc across G × E × M interactions.EEA BalcarceFil: Bohman, Brian J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos.Fil: Culshaw-Maurer, Michael J. University of Arizona. CyVerse; Estados Unidos.Fil: Abdallah, Feriel Ben. Walloon Agricultural Research Centre. Productions in Agriculture Department, Crop Production Unit, Bélgica.Fil: Giletto, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Unidad Integrada Balcarce. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Bélanger, Gilles. Science and Technology Branch, Agriculture and Agri-Food Canada; Canadá.Fil: Fernández, Fabián G. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos.Fil: Miao, Yuxin. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos.Fil: Mulla, David J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos.Fil: Rosen, Carl J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos

Version control.

Public and private version control organizations on GitHub and GitLab for CyVerse Software, Public Container Registry, and Education. (PDF)</p

University of Arizona hardware.

On-premises resources maintained by CyVerse at the University of Arizona. DE = Discovery Environment, VICE = Virtual Interactive Compute Environment. (PDF)</p

Glossary.

Frequently used abbreviations and acronyms with descriptions. (PDF)</p

Multiplier effects.

Only 25% of NSF awards which requested Letters of Collaboration (LOCs) mention “CyVerse” or “iPlant Collaborative” in their public abstract. Of the total awards that mention “CyVerse” or “iPlant Collaborative” 73% did not request LOCs.</p