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豌豆可根据环境变化做出适应性选择

2017-08-21 10:54 |  来源:咕噜英语 |  人气: | 


     
      An international team of scientists from Oxford University, UK, and Tel-Hai College, Israel, has shown that pea plants can demonstrate sensitivity to risk - namely, environmental variance, unknown outside the animal kingdom. In the study, published in the journal Current Biology, pea plants were grown with their roots split between two pots, facing the decision of which pot to prioritise.
     In a preliminary experiment, the researchers showed that the plants grew more roots in a pot endowed with higher levels of nutrients - an adaptive response similar to animals allocating greater foraging effort to richer food patches. In a series of follow-up experiments, they then split the roots of each plant between two pots that had equal average nutrient concentrations, but where one pot had a constant level and the other a variable level, asking whether plants would 'prefer' to grow more roots in one or the other.
     Based on theoretical analyses of how decision makers such as humans or animals respond to similar choices, the researchers predicted that plants might prefer the variable pot when the average nutrient level was low, and the constant pot when average nutrient level was high.
      when the average nutrient level is below what is required for the plant to thrive, the variable option at least offers the chance to 'gamble' on a run of good luck. On the other hand, when average conditions are good, it makes sense to take the safe option.
     The researchers found that this is exactly what the pea plants did.
     Co-author Professor Alex Kacelnik, of the Department of Zoology at Oxford University, said: 'To our knowledge, for humans or other animals, and interesting behaviours can theoretically be predicted as biological adaptations - - on the basis of processes evolved to exploit natural opportunities efficiently.
     'We do not yet know how the plants' sense variance functions, or even if their physiology is specifically adapted to respond to risk, to look e as one would model an intelligent agent.'
     
     
     
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