Genetic analysis of east and west coast populations of Penaeus monodon from India based on random amplified polymorphic DNA

The east and west coast populations of wild Penaeus monodon in India were genetically characterized by RAPD analysis using six highly polymorphic primers reported earlier. The average genetic similarities within populations, based on profiles generated by all the six primers, were 0.828 and 0.851 for the east and west coast populations, respectively, values with individual primers ranging from 0.744 to 0.889. The average genetic similarity between populations across all the primers was 0.774. The number of bands found to be polymorphic were 38 (51.35%) and 37 (50.68%) in the east and west coast populations, respectively. Primer 5 yielded the highest level of polymorphism (63.63%) in the east coast population whereas primer 3 yielded the lowest level of polymorphism (36.36%) in the west coast population. The study reveals the existence of genetic variation in P. monodon stocks providing scope for genetic improvement through selective breeding. It also provides baseline data for future work on population structure analysis of P. monodon.

Sleeping sites, sleeping trees, and sleep-related behaviors of black crested gibbons (Nomascus concolor jingdongensis) at Mt. Wuliang, central Yunnan, China

Data on sleep-related behaviors were collected for a group of central Yunnan black crested gibbons (Nomascus concolor jingdongensis) at Mt. Wuliang, Yunnan, China from March 2005 to April 2006. Members of the group usually formed four sleeping units (adult male and juvenile, adult female with one semi-dependent black infant, adult female with one dependent yellow infant, and subadult male) spread over different sleeping trees. Individuals or units preferred specific areas to sleep; all sleeping sites were situated in primary forest, mostly (77%) between 2,200 and 2,400 m in elevation. They tended to sleep in the tallest and thickest trees with large crowns on steep slopes and near important food patches. Factors influencing sleeping site selection were (1) tree characteristics, (2) accessibility, and (3) easy escape. Few sleeping trees were used repeatedly by the same or other members of the group. The gibbons entered the sleeping trees on average 128 min before sunset and left the sleeping trees on average 33 min after sunrise. The lag between the first and last individual entering the trees was on average 17.8 min. We suggest that sleep-related behaviors are primarily adaptations to minimize the risk of being detected by predators. Sleeping trees may be chosen to make approach and attack difficult for the predator, and to provide an easy escape route in the dark. In response to cold temperatures in a higher habitat, gibbons usually sit and huddle together during the night, and in the cold season they tend to sleep on ferns and/or orchids.

Facilitation of learning induced by both random and gradual visuomotor task variation.

Motor task variation has been shown to be a key ingredient in skill transfer, retention, and structural learning. However, many studies only compare training of randomly varying tasks to either blocked or null training, and it is not clear how experiencing different nonrandom temporal orderings of tasks might affect the learning process. Here we study learning in human subjects who experience the same set of visuomotor rotations, evenly spaced between -60° and +60°, either in a random order or in an order in which the rotation angle changed gradually. We compared subsequent learning of three test blocks of +30°→-30°→+30° rotations. The groups that underwent either random or gradual training showed significant (P < 0.01) facilitation of learning in the test blocks compared with a control group who had not experienced any visuomotor rotations before. We also found that movement initiation times in the random group during the test blocks were significantly (P < 0.05) lower than for the gradual or the control group. When we fit a state-space model with fast and slow learning processes to our data, we found that the differences in performance in the test block were consistent with the gradual or random task variation changing the learning and retention rates of only the fast learning process. Such adaptation of learning rates may be a key feature of ongoing meta-learning processes. Our results therefore suggest that both gradual and random task variation can induce meta-learning and that random learning has an advantage in terms of shorter initiation times, suggesting less reliance on cognitive processes.