Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N–S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.

植物物种的物候变化是气候变化对生物圈影响的最显着迹象之一。由于当地适应性对地理种群的气候要求各不相同，因此对广阔区域内植物物种的物候变化进行建模具有挑战性。在这项研究中，我们开发了一种经验方法，使用纬度作为预测因子来校准温带树木的物候模型，以解释种群对 N-S 温度梯度的局部适应性。使用来自美国国家物候网络的数据调查了美国东部 14 种广泛分布的树种。我们在基本的热时间萌芽模型中实现了该方法，以介绍该方法的算法并测试其有效性。对于每个物种，首先使用传统的非空间模型预测折断叶芽的日期，然后使用具有针对不同纬度的不同种群校准的临界热强迫要求的空间模型。正如预期的那样，假设跨纬度的统一强迫要求的非空间模型预测在较高（高估 - 预测较晚）和较低（低估 - 预测较早）纬度均显示出一致和系统的偏差。使用我们的方法校准的空间模型消除了地理偏差，并产生了与观测值更接近的纬度梯度。空间模型还将整体预测误差从 32.2 天的平均均方根误差 (RMSE) 减少到 20 天。训练数据集为 4 天，测试数据集的预测平均均方根误差 (RMSEP) 为 32.2 天至 19.9 天。本文重点介绍新的校准方法，作为开发操作模型的准备步骤，该模型可能潜在地预测各种植物物种对气候变化的大规模和范围广泛的物候响应，并提高局部精度。