Applicability of Dynamic Energy Budget (DEB) models across steep environmental gradients
Applicability of Dynamic Energy Budget (DEB) models across steep environmental gradients
复制标题
动态能源预算 (DEB) 模型在陡峭环境梯度中的适用性
作者：
C. Monaco;C. McQuaid
稳健的生态预测需要准确预测对环境驱动因素的生理反应。能源收支模型通过机械地将生物与非生物驱动因素联系起来，促进了这一点，但通常在相对稳定的物理条件下是真实的，忽略了时间/空间环境的变异性。动态能量预算(DEB)理论是一个强大的框架，能够将个人健康与环境驱动因素联系起来，我们通过检查岩石海岸的模型预测来测试其适应变化的能力，岩石海岸是一个陡峭的交错带，其特征是温度和食物供应的广泛波动。我们对南非南海岸共存的中、高海岸贻贝(Mytilus Galloporcialis)和中、下海岸贻贝(Perna Perna)的DEB模型进行了参数化。首先，我们假设了永久淹没的条件，然后结合了低潮条件下的代谢抑制，使用了三个地点超过12个月的潮汐周期、体温和食物变异性的详细数据。模型对这两个物种在海岸的壳长提供了很好的估计，但对性腺指数的预测一直低于观测结果。模型的不一致可能反映生物学细节的影响和/或捕捉环境变异性的困难，强调需要将两者结合起来。我们的方法提供了将环境变异性和长期变化纳入机械模型以改进生态预测的指导方针。
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.