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首页> 外文期刊>The Journal of Experimental Biology >Thermal variation, thermal extremes and the physiological performance of individuals
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Thermal variation, thermal extremes and the physiological performance of individuals

机译:温度变化,极端温度和个体的生理表现

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In this review we consider how small-scale temporal and spatial variation in body temperature, and biochemical/physiological variation among individuals, affect the prediction of organisms' performance in nature. For 'normal' body temperatures - benign temperatures near the species' mean - thermal biology traditionally uses performance curves to describe how physiological capabilities vary with temperature. However, these curves, which are typically measured under static laboratory conditions, can yield incomplete or inaccurate predictions of how organisms respond to natural patterns of temperature variation. For example, scale transition theory predicts that, in a variable environment, peak average performance is lower and occurs at a lower mean temperature than the peak of statically measured performance. We also demonstrate that temporal variation in performance is minimized near this new 'optimal' temperature. These factors add complexity to predictions of the consequences of climate change. We then move beyond the performance curve approach to consider the effects of rare, extreme temperatures. A statistical procedure (the environmental bootstrap) allows for long-term simulations that capture the temporal pattern of extremes (a Poisson interval distribution), which is characterized by clusters of events interspersed with long intervals of benign conditions. The bootstrap can be combined with biophysical models to incorporate temporal, spatial and physiological variation into evolutionary models of thermal tolerance. We conclude with several challenges that must be overcome to more fully develop our understanding of thermal performance in the context of a changing climate by explicitly considering different forms of small-scale variation. These challenges highlight the need to empirically and rigorously test existing theories.
机译:在这篇综述中,我们考虑了体温的小范围时空变化以及个体之间的生化/生理变化如何影响对自然界中生物表现的预测。对于“正常”的体温-接近物种平均温度的良性温度-传统上,热生物学使用性能曲线来描述生理能力如何随温度变化。但是,这些通常在静态实验室条件下测量的曲线可能无法完全预测生物体对自然温度变化的反应方式。例如,尺度转换理论预测,在可变的环境中,峰值平均性能要比静态测量的性能峰值低,并且平均温度低于静态测量的性能峰值。我们还证明,在这个新的“最佳”温度附近,性能的时间变化最小。这些因素增加了对气候变化后果的预测的复杂性。然后,我们超越了性能曲线方法,来考虑罕见的极端温度的影响。统计过程(环境引导程序)允许进行长期模拟,以捕获极端事件的时间模式(泊松区间分布),其特征是事件簇散布着良性条件的较长间隔。引导程序可以与生物物理模型结合,以将时间,空间和生理变化纳入热耐受性进化模型中。通过明确考虑不同形式的小规模变化,我们将面临几个挑战,必须克服这些挑战才能更充分地发展我们在气候变化背景下对热性能的理解。这些挑战凸显了对经验理论进行严格检验的必要性。

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