Temporal Variation in the Niche Partitioning of Lake Michigan Salmonines as it Relates to Alewife Abundance and Size Structure.

Stable isotope analyses offer a useful means for quantifying ecological niche dimensions, though few studies have examined isotopic response of an ecological community with respect to resource gradients such as fluctuations in prey availability. Stable carbon and nitrogen isotopes were measured for Lake Michigan salmonines and their prey collected from 2014 to 2016. Bayesian ellipse and mixing model analyses were used to quantify isotopic niche characteristics and diets, respectively, among species and years. During the three-year study period, abundance and size structure of preferred alewife prey changed substantially and offered an opportunity to explore predator isotopic niche response and diet shifts along a prey resource gradient. Results suggested increased reliance on alewives, especially small alewives, over the study period and were consistent with greater availability of this prey. However, differential use of alewife size classes and alternative prey sources by salmonine predators was apparent, which suggested possible resource partitioning. Characterization of ecological niche overlap using stable isotopes likely requires consideration of shared resource availability as well as specific prey and habitat preferences.

Habitat-dependent effects of predatory spiders on prey frogs in a Neotropical wet forest

In seasonal wet Neotropical forests, many studies have suggested that species-rich terrestrial frog assemblages are regulated bottom-up by the abundance of leaf litter. However, terrestrial frogs are prey to a diverse community of predators, and no studies have tested for top-down effects of predators on this or other anuran assemblages. Here, we used an extensive field dataset to model the relative contribution of food resources, microhabitat resources and predators towards the occupancy and detection of two frog species (Craugastor bransfordii and Oophaga pumilio) at La Selva, Costa Rica. Frog occupancy was most strongly influenced by predatory spiders and secondarily influenced by the abundance of leaf litter. Predators exerted stronger effects on frogs than food resources, and frogs avoided predators more as leaf litter decreased. Detection probability was elevated when predators were present. We found support for bottom-up effects of leaf litter on the terrestrial frog assemblage, but top-down effects by predators exerted stronger effects on frog occupancy and detection. Because predator avoidance varied along a resource gradient, predator and resource effects appear to be dependent, supporting interactions between top-down and bottom-up mechanisms. Climate-driven decreases in leaf litter may drive decreased availability of frog refugia and increased interactions between frogs and predators.

Bootstrapped Motion of an Agent on an Adaptive Resource Landscape

We theoretically show that isolated agents that locally and symmetrically consume resources and sense positive resource gradients can generate constant motion via bootstrapped resource gradients in the absence of any externally imposed gradients, and we show a realization of this motion using robots. This self-generated agent motion can be coupled with neighboring agents to act as a spontaneously broken symmetry seed for emergent collective dynamics. We also show that in a sufficiently weak externally imposed gradient, it is possible for an agent to move against an external resource gradient due to the local resource depression on the landscape created by an agent. This counter-intuitive boot-strapped motion against an external gradient is demonstrated with a simple robot system on an light-emitting diode (LED) light-board.

Optimal foraging and the information theory of gambling

At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration.

Optimal foraging and the information theory of gambling

At a macroscopic level, part of the ant colony life-cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimised by a strategy of betting in proportion to the probability of payoff. Thus, in the case of ants the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum generates predictions for which collective ant movement strategies might have evolved. Here, we show how colony level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo methods, specifically Hamiltonian Markov chain Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) the resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understanding movement ecology, and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient, and matches a number of characteristics of real ant exploration.