Sex allocation conflict in ants: when the queen rules.

Insect societies are paramount examples of cooperation, yet they also harbor internal conflicts whose resolution depends on the power of the opponents. The male-haploid, female-diploid sex-determining system of ants causes workers to be more related to sisters than to brothers, whereas queens are equally related to daughters and sons. Workers should thus allocate more resources to females than to males, while queens should favor an equal investment in each sex. Female-biased sex allocation and manipulation of the sex ratio during brood development suggest that workers prevail in many ant species. Here, we show that queens of Formica selysi strongly influenced colony sex allocation by biasing the sex ratio of their eggs. Most colonies specialized in the production of a single sex. Queens in female-specialist colonies laid a high proportion of diploid eggs, whereas queens in male-specialist colonies laid almost exclusively haploid eggs, which constrains worker manipulation. However, the change in sex ratio between the egg and pupae stages suggests that workers eliminated some male brood, and the population sex-investment ratio was between the queens' and workers' equilibria. Altogether, these data provide evidence for an ongoing conflict between queens and workers, with a prominent influence of queens as a result of their control of egg sex ratio.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Eighty-Sixth General Assembly Joint Rules of the House and Senate

Eighty-Sixth General Assembly Joint Rules of the House and Senate (House Concurrent Resolution 6), House adopted 2-3-2015, Senate adopted 2-4-2015

Eighty-Sixth General Assembly House Rules (House Resolution 4-Adopted 2-3-2015), February 3, 2015

Eighty-Sixth General Assembly House Rules (House Resolution 4-Adopted 2-3-2015)

Eighty-Sixth General Assembly Joint Rules Governing Lobbyists (House Concurrent Resolution 7) House Adopted 2-3-2015, Senate Adopted 2-4-2015, February 5, 2015

Eighty-Sixth General Assembly Joint Rules Governing Lobbyists (House Concurrent Resolution 7) House Adopted 2-3-2015, Senate Adopted 2-4-2015

Eighty-Sixth General Assembly Senate Rules (Senate Resolution 1 — Adopted 2/4/15), February 6, 2015

Eighty-Sixth General Assembly House Rules (House Resolution 4-Adopted 2-3-2015)

Audit report on Taylor County, Iowa for the year ended June 30, 2015

Audit report on Taylor County, Iowa for the year ended June 30, 2015