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Settlement is a critical transition in the life history of reef fish, and the timing of this event can have a strong effect on fitness. Key factors that influence settlement timing are predictable lunar cyclic variation in tidal currents, moonlight, and nocturnal predation risk as larvae transition from pelagic to benthic environments. However, populations typically display wide variation in the arrival of settlers over the lunar cycle. This variation is often hypothesized to result from unpredictable conditions in the pelagic environment and bet-hedging by spawning adults. Here, we consider the hypothesis that the timing of spawning and settlement is a strategic response to post-settlement competition. We use a game theoretic model to predict spawning and settlement distributions when fish face a tradeoff between minimizing density-independent predation risk while crossing the reef crest vs. avoiding high competitor density on settlement habitat. In general, we expect competition to spread spawning over time such that settlement is distributed around the lunar phase with the lowest predation risk, similar to an ideal free distribution in which competition spreads competitors across space. We examine the effects of overcompensating density dependence, age-dependent competition, and competition among daily settler cohorts. Our model predicts that even in the absence of stochastic variation in the larval environment, competition can result in qualitative divergence between spawning and settlement distributions. Furthermore, we show that if competitive strength increases with settler age, competition results in covariation between settler age and settlement date, with older larvae settling when predation risk is minimal. We predict that competition between daily cohorts delays peak settlement, with priority effects potentially selecting for a multimodal settlement distribution. [ABSTRACT FROM AUTHOR]

Abstract: Toxicant bioaccumulation poses a risk to many marine mammal populations. Although individual-level toxicology has been the subject of considerable research in several species, we lack a theoretical framework to generalize the results across environments and life histories. Here we formulate a dynamic energy budget model to predict the effects of intra- and interspecific life history variation on toxicant dynamics in marine mammals. Dynamic energy budget theory attempts to describe the most general processes of energy acquisition and utilization in heterotrophs. We tailor the basic model to represent the marine mammal reproductive cycle, and we add a model of toxicant uptake and partitioning to describe vertical transfer of toxicants from mother to offspring during gestation and lactation. We first show that the model predictions are consistent with qualitative patterns reported in empirical studies and previous species-specific modeling studies. Next, we use this model to examine the dependence of offspring toxicant load on birth order, food density, and interspecific life history variation. [Copyright &y& Elsevier]

Abstract: We develop a suite of models with varying complexity to predict elk movement behavior during the winter on the Northern Range of Yellowstone National Park (YNP). The models range from a simple representation of optimal patch choice to a dynamic game, and we show how the underlying theory in each is related by the presence or absence of state- and frequency-dependence. We compare predictions from each of the models for three variables that are of basic and applied interest: elk survival, aggregation, and use of habitat outside YNP. Our results suggest that despite low overall forage depletion in the winter, frequency-dependence is crucial to the predictions for elk movement and distribution. Furthermore, frequency-dependence interacts with mass-dependence in the predicted outcome of elk decision-making. We use these results to show how models that treat single movement decisions in isolation from the seasonal sequence of decisions are insufficient to capture landscape scale behavior. [Copyright &y& Elsevier]