University of California, Davis

About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Gene. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.

Fear in animals: a meta-analysis and review of risk assessment

Abstract: The amount of risk animals perceive in a given circumstance (i.e. their degree of ‘fear’) is a difficult motivational state to study. While many studies have used flight initiation distance as a proxy for fearfulness and examined the factors influencing the decision to flee, there is no general understanding of the relative importance of these factors. By identifying factors with large effect sizes, we can determine whether anti-predator strategies reduce fear, and we gain a unique perspective on the coevolution of predator and anti-predator behaviour. Based on an extensive review and formal meta-analysis, we found that predator traits that were associated with greater risk (speed, size, directness of approach), increased prey distance to refuge and experience with predators consistently amplified the perception of risk (in terms of flight initiation distance). While fish tolerated closer approach when in larger schools, other taxa had greater flight initiation distances when in larger groups. The presence of armoured and cryptic morphologies decreased perception of risk, but body temperature in lizards had no robust effect on flight initiation distance. We find that selection generally acts on prey to be sensitive to predator behaviour, as well as on prey to modify their behaviour and morphology.

An optimized fluorescent probe for visualizing glutamate neurotransmission

Abstract: We describe an intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) with signal-to-noise ratio and kinetics appropriate for in vivo imaging. We engineered iGluSnFR in vitro to maximize its fluorescence change, and we validated its utility for visualizing glutamate release by neurons and astrocytes in increasingly intact neurological systems. In hippocampal culture, iGluSnFR detected single field stimulus-evoked glutamate release events. In pyramidal neurons in acute brain slices, glutamate uncaging at single spines showed that iGluSnFR responds robustly and specifically to glutamate in situ, and responses correlate with voltage changes. In mouse retina, iGluSnFR-expressing neurons showed intact light-evoked excitatory currents, and the sensor revealed tonic glutamate signaling in response to light stimuli. In worms, glutamate signals preceded and predicted postsynaptic calcium transients. In zebrafish, iGluSnFR revealed spatial organization of direction-selective synaptic activity in the optic tectum. Finally, in mouse forelimb motor cortex, iGluSnFR expression in layer V pyramidal neurons revealed task-dependent single-spine activity during running.

Dislocation processes in the deformation of nanocrystalline aluminium by molecular-dynamics simulation.

Abstract: The mechanical behaviour of nanocrystalline materials (that is, polycrystals with a grain size of less than 100 nm) remains controversial. Although it is commonly accepted that the intrinsic deformation behaviour of these materials arises from the interplay between dislocation and grain-boundary processes, little is known about the specific deformation mechanisms. Here we use large-scale molecular-dynamics simulations to elucidate this intricate interplay during room-temperature plastic deformation of model nanocrystalline Al microstructures. We demonstrate that, in contrast to coarse-grained Al, mechanical twinning may play an important role in the deformation behaviour of nanocrystalline Al. Our results illustrate that this type of simulation has now advanced to a level where it provides a powerful new tool for elucidating and quantifying--in a degree of detail not possible experimentally--the atomic-level mechanisms controlling the complex dislocation and grain-boundary processes in heavily deformed materials with a submicrometre grain size.

The importance of correcting for sampling bias in MaxEnt species distribution models

Abstract: Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo. Location Borneo, Southeast Asia. Methods We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas. Results Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased. Main Conclusions We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.