Behavioural Plasticity by Eastern Grey Kangaroos inResponse to Human Behaviour.

Sharing landscapes with humans is an increasingly fraught challenge for wildlife acrossthe globe. While some species benefit from humans by exploiting novel opportunities (e.g., provisionof resources or removal of competitors or predators), many wildlife experience harmful effects, eitherdirectly through persecution or indirectly through loss of habitat. Consequently, some species havebeen shown to be attracted to human presence while others avoid us. For any given populationof a single species, though, the question of whether they can recognise and change their responseto human presence depending on the type of human actions (i.e., either positive or negative) hasreceived little attention to date. In this study, we chose to examine the behavioural plasticity withina single population of eastern grey kangaroos (Macropus giganteus) to both positive and negativehuman activity. Within a relatively small and contiguous landscape, we identified areas wherekangaroos experience a combination of either low and high frequencies of benign and harmfulhuman disturbances. From six sampling sessions over five months, we found that density and groupsizes were higher where humans acted benignly towards them, and that these groups had higherrepresentations of sub-adults and juveniles than where humans had harmful intentions. Importantly,we found that the vital antipredator strategy of increasing group size with distance from cover wasnot detectable at sites with low and high levels of harm. Our findings suggest that these kangaroosare recognising and adjusting their behavioural response to humans at fine spatial scales, a plasticitytrait that may be key to the survival of these species in human dominated landscapes.

Flight responses of eastern gray kangaroos to benign or harmful human behavior.

Globally, wilderness is being converted for rural and agricultural land use. In countryside landscapes, many habitat structures remain intact, providing suitable habitat for wildlife species that can accurately assess novel risks and develop tolerance to benign disturbances. Associative learning that promotes avoidance and also facilitates desensitization to benign disturbance is key to persisting in these landscapes. Conversely, learning to distinguish and avoid negative interactions with humans, like hunting, is vital. To determine if eastern gray kangaroos are capable of learning from previous interactions with humans, we tested the flight responses of wild kangaroos which have previously experienced either low or high frequencies of harmful and benign encounters with humans. We found that eastern gray kangaroos rapidly habituated to benign disturbance as there was no significant difference in assessment distance between groups that previously experienced low or high frequencies of disturbance. The threat of harmful disturbances was not as quickly learnt, as groups that experienced low frequencies of harmful disturbance delayed flight longer than those experiencing frequent harm. We found that the influence of environmental and group parameters on a kangaroo's decision to flee depended on the intent and frequency of previous interactions with humans. Our study indicates that kangaroos are learning from previous encounters with humans, correctly assessing novel risks which may be contributing to their persistence in countryside landscapes.

Efficient Sampling of Bacterial Signal Transduction for Detection of Pulse-Amplitude Modulated Molecular Signals.

The sampling of the bacterial signal transduction is investigated for molecular communication (MC). It is assumed that the finite-duration amplitude modulated, i.e., pulse-amplitude modulated (PAM), concentration of a certain type of molecule is used for information transmission. The bacterial signaling pathway is modified to transduce the input molecules to the output signal, i.e., produce green fluorescent protein (GFP). The bacterial signal transduction is composed of a set of biochemical reactions which impose randomness on the response. Therefore, the input-output relation, the timing issues, and the noise effects for the bacteria response are characterized based on both analytical and experimental observations. Sampling schemes for the raw bacteria response are proposed based on the total response duration, the peak value, the ramp-up slope, and the ramp-down slope. Each sampling scheme is shown to be providing a one-to-one and monotonic function of the input. The sampling based on the ramp-up slope is shown to be statistically favorable for the detection of PAM molecular signals. Accordingly, the time interval selection and non-coherent sampling are studied for the efficient calculation of the ramp-up slope from the raw bacteria response. This work provides a basis for the sampling of the raw bacteria response and enables accurate detection of PAM molecular signals via bacterial response for MC and sensing applications.

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments.

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 µM-30 µM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices.