Organisms cope with environmental stressors by behavioral, morphological, and physiological adjustments. Documentation of such adjustments in the wild provides information on the response space in nature and the extent to which behavioral and bodily adjustments lead to appropriate performance effects. Here we studied the morphological and digestive adjustments in a staging population of migrating Great Knots Calidris tenuirostris in response to stark declines in food abundance and quality at the Yalu Jiang estuarine wetland (northern Yellow Sea, China). At Yalu Jiang, from 2011 to 2017 the densities of intertidal mollusks, the food of Great Knots, declined 15‐fold. The staple prey of Great Knots shifted from the relatively soft‐shelled bivalve Potamocorbula laevis in 2011–2012 to harder‐shelled mollusks such as the gastropod Umbonium thomasi in 2016–2017. The crushing of the mollusks in the gizzard would require a threefold to 11‐fold increase in break force. This was partially resolved by a 15% increase in gizzard mass which would yield a 32% increase in shell processing capacity. The consumption of harder‐shelled mollusks was also accompanied by reliance on regurgitates to excrete unbreakable parts of prey, rather than the usual intestinal voidance of shell fragments as feces. Despite the changes in digestive morphology and strategy, there was still an 85% reduction in intake rate in 2016–2017 compared with 2011–2012. With these morphological and digestive adjustments, the Great Knots remaining faithful to the staging site to a certain extent buffered the disadvantageous effects of dramatic food declines. However, compensation was not complete. Locally, birds will have had to extend foraging time and use a greater daily foraging range. This study offers a perspective on how individual animals may mitigate the effects of environmental change by morphological and digestive strategies and the limits to the response space of long‐distance migrating shorebirds in the wild.

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An enhanced operational amplifier (OPAMP) macro model based on artificial neural network (ANN) is developed for analog circuit simulation. The model uses ANN to capture the relation between the inputs and outputs (currents and voltages) of the circuit module. Both direct current (DC) and alternative current (AC) signals are considered in the model. By adopting the adaptive sampling algorithm, the amount of data required for model training can be significantly reduced and the accuracy of model fitting can be apparently improved. The model is also validated by the simulated data from OPAMP, Bandgap, and LDO circuits. Compared with SPICE model, enhanced ANN OPAMP macro model has nearly 8 times faster simulation speed without apparently degrading the model accuracy. The predictions made by the neural network are also compared to the experimental measurement results of LDO circuit fabricated in 0.18‐μm process. Both simulation and experimental results show the feasibility and accuracy of the proposed enhanced ANN OPAMP macro model.