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Applicability of Dynamic Energy Budget (DEB) models across steep environmental gradients

机译:动态能源预算(DEB)模型在陡峭的环境梯度中的适用性

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摘要

Robust ecological forecasting requires accurate predictions of physiological responses to environmental drivers. Energy budget models facilitate this by mechanistically linking biology to abiotic drivers, but are usually ground-truthed under relatively stable physical conditions, omitting temporal/spatial environmental variability. Dynamic Energy Budget (DEB) theory is a powerful framework capable of linking individual fitness to environmental drivers and we tested its ability to accommodate variability by examining model predictions across the rocky shore, a steep ecotone characterized by wide fluctuations in temperature and food availability. We parameterized DEB models for co-existing mid/high-shore (Mytilus galloprovincialis) and mid/low-shore (Perna perna) mussels on the south coast of South Africa. First, we assumed permanently submerged conditions, and then incorporated metabolic depression under low tide conditions, using detailed data of tidal cycles, body temperature and variability in food over 12 months at three sites. Models provided good estimates of shell length for both species across the shore, but predictions of gonadosomatic index were consistently lower than observed. Model disagreement could reflect the effects of details of biology and/or difficulties in capturing environmental variability, emphasising the need to incorporate both. Our approach provides guidelines for incorporating environmental variability and long-term change into mechanistic models to improve ecological predictions.
机译:强大的生态预测要求对环境驱动因素的生理反应进行准确的预测。能量预算模型通过将生物学与非生物驱动因素机械地联系起来而促进了这一过程,但是通常在相对稳定的物理条件下进行实地分析,从而忽略了时间/空间环境的可变性。动态能源预算(DEB)理论是一个强大的框架,能够将个体适应性与环境驱动因素联系起来,我们通过检查多岩石海岸上的模型预测来测试其适应变化的能力,该陡峭的过渡带具有温度和食物供应量的广泛波动。我们为南非南部海岸的中/上岸(Mytilus galloprovincialis)和中/下岸(Perna perna)贻贝共存的DEB模型参数化。首先,我们使用潮汐周期,体温和三个地点的食物在12个月内的变异性的详细数据,假设处于永久性淹没状态,然后在低潮条件下纳入代谢抑制。这些模型对沿岸的两个物种的壳长提供了很好的估计,但是对性腺体指数的预测始终低于观测值。模型分歧可能反映出生物学细节的影响和/或捕获环境可变性方面的困难,强调需要将两者结合在一起。我们的方法为将环境可变性和长期变化纳入机械模型以改善生态预测提供了指导。

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