Predicting the impact of carnivores on plants has challenged community and food web ecologists for decades. At the same time, the role of predators in the evolution of herbivore dietary specialization has been an unresolved issue in evolutionary ecology. Here, we integrate these perspectives by testing the role of herbivore diet breadth as a predictor of top-down effects of avian predators on herbivores and plants in a forest food web. Using experimental bird exclosures to study a complex community of trees, caterpillars, and birds, we found a robust positive association between caterpillar diet breadth (phylodiversity of host plants used) and the strength of bird predation across 41 caterpillar and eight tree species. Dietary specialization was associated with increased enemy-free space for both camouflaged (n = 33) and warningly signaled (n = 8) caterpillar species. Furthermore, dietary specialization was associated with increased crypsis (camouflaged species only) and more stereotyped resting poses (camouflaged and warningly signaled species), but was unrelated to caterpillar body size. These dynamics in turn cascaded down to plants: a metaanalysis (n = 15 tree species) showed the beneficial effect of birds on trees (i.e., reduced leaf damage) decreased with the proportion of dietary specialist taxa composing a tree species' herbivore fauna. We conclude that herbivore diet breadth is a key functional trait underlying the trophic effects of carnivores on both herbivores and plants.ecological specialization | host specificity | plant-herbivore interactions | tritrophic interactions | trophic cascade P redicting the strength of trophic interactions is a major goal in ecology. Because most natural ecosystems contain numerous coexisting species at each trophic level, achieving this goal necessarily involves the integration of theory in evolutionary, community, and food web ecology. In this context, evolutionary ecology explains how traits of organisms adapt them to a fundamental trade-off between resource acquisition and mortality risk from natural enemies (1, 2); community ecology theory links the many patterns and consequences of species interactions to the diversity of traits of those species (2); and food web ecology subsumes this diversity into patterns of trophic structure and dynamics, such as a trophic cascade (3). The recognition that functional traits of species can drive the indirect positive effect of carnivores on plant biomass [trophic cascades broadly defined (4, 5)] provides important insight into the causes of variation in these dynamics (1,6). An emerging understanding of the functional traits mediating trophic cascade strength includes traits of herbivores that facilitate predator avoidance (7-10), or provide constitutive (11, 12) or induced resistance to predation (13). These examples identify antipredator traits of herbivores as an important mediator of top-down effects on plants within individual tritrophic food chains. However, the role of antipredator (or other) traits of herbivores is curr...

We consider the Dynamic Map Visitation Problem (DMVP), in which a team of agents must visit a collection of critical locations as quickly as possible, in an environment that may change rapidly and unpredictably during the agents' navigation. We apply recent formulations of time-varying graphs (TVGs) to DMVP, shedding new light on the computational hierarchy R ⊃ B ⊃ P of TVG classes by analyzing them in the context of graph navigation. We provide hardness results for all three classes, and for several restricted topologies, we show a separation between the classes by showing severe inapproximability in R, limited approximability in B, and tractability in P. We also give topologies in which DMVP in R is fixed parameter tractable, which may serve as a first step toward fully characterizing the features that make DMVP

The effectiveness of anti‐predator traits, such as warning signals and camouflage, has rarely been quantified from a phylogenetic community ecology perspective. Here we use a phylogenetic comparative analysis to test the association between several putative anti‐predator traits and bird predation risk in an assemblage of caterpillar species. We synthesize eight years of field and laboratory study of a temperate forest community, including a four‐year bird exclusion experiment that provided comparative measures of bird predation risk for 38 caterpillar species from a phylogenetic community. We then conducted a phylogenetic generalized least‐squares and information‐theoretic model selection analysis of warning signals (aposematism or mimicry), camouflage (crypsis or masquerade), and behavioral responses to physical attack as predictors of bird predation, while also accounting for putatively important effects of the abundance, mean body size, and phenology of caterpillar species. The most behaviorally specialized caterpillar species possessing warning signals experienced the lowest bird predation risk, supporting aposematism theory and highlighting the role of prey behavior in the visual signaling of predators. Among the camouflaged caterpillar species, those with the greatest latency to detection by human proxy predators experienced the lowest bird predation risk, supporting camouflage theory. Caterpillar behavioral responses to physical attack, however, predicted increased bird predation risk among camouflaged caterpillars. Although caterpillar abundance, body size, and phenology were expected to be important based on inference from optimal foraging theory and previous field observations, these factors had limited predictive power. This study provides methodologically unique evidence for the importance of morphological and behavioral components of primary, visual defenses of caterpillars against their avian predators in a natural community.

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments. r

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In this paper, we consider the potential for reasoning about animations in the language of hybrid dynamical systems (i.e., systems with both continuous and discrete dynamics). We begin by directly applying hybrid systems theory to animation, using a general-purpose hybrid system specification tool to generate multi-agent animations; this application also illustrates that hybrid system models can provide systematic modular ways to incorporate low-level behavior into a design for higher-level behavioral modeling. We then apply the logical framework of hybrid systems to animation: we formally state properties of animation systems that may not be readily expressed in other frameworks; and we mechanically check a collision-avoidance property for a simple race-like game. This hybrid systemsoriented approach could improve our ability to reason about virtual worlds, thus improving our ability to create intelligent virtual agents.

The genetic operators (GOs) of recombination, mutation, and selection are commonly included in studies of evolution and evolvability, but they are not the only operators that can affect the genotype-to-phenotype (G → P) map and thus the outcomes of evolution. In this paper, we present experiments with an epigenetic operator (EO), interactive wiring of a circuit, alongside common GOs, investigating both epigenetic and GO effects on the evolution of both simulated and physically embodied Braitenberg-inspired robots. As a platform for our experiments, we built a system that encoded the genetics for the physical circuitry of the analog robots and made explicit rules for how that circuitry would be constructed; phenotypic expression consisted of the placement of wires to form the circuitry and thus govern robot behavior. We then varied the presence of gene interactions across populations of robots, studying how the EO-and its effects on G → P maps-affected the results of evolution over several generations. Additionally, a variant of these experiments was run in simulation to provide an independent test of the evolutionary impact of this EO. Our results demonstrate that robot populations with the EO had quantitatively different and potentially less adaptive evolution than populations without it. For example, selection increased the rate at which functional circuitry was lost in the population with the EO, compared to the population without it. In addition, in simulation, EO populations were significantly less fit than populations without it. More generally, results such as these demonstrate the interaction of genetic and EOs during evolution, suggesting the broad importance of including EOs in investigations of evolvability. To our knowledge, our work represents the first physically embodied EO to be used in the evolution of physically embodied robots.