Changes in butterfly phenology due to climate changes have led to the need for models based on factors other than calendar date to predict butterfly development, allowing those monitoring their populations to increase the effectiveness of field surveys. In this study, we developed two simple climatic models, one using yearly accumulated growing degree days (GDD) and the other using yearly accumulated shortwave radiation flux densities (SRAD), to determine if these variables can predict first emergence of three butterfly species with less error than an approach based on the average ordinal date of first observation at a site. Furthermore, we investigated whether combining our two models would increase our ability to predict the timing of first emergence. We determined that GDD models were better at predicting first emergence than were ordinal date models and SRAD models for all species tested; however, the actual variation among these models was so small that any additional effort required to develop GDD models would not justify their use as a replacement for the simpler ordinal date models at this time, although as climate changes they may become more useful. We also determined that combined models did not improve the ability to predict first emergence.

中文翻译：

---

气候变量能否改善蝴蝶物种的物候预测？

由于气候变化引起的蝴蝶物候变化导致需要基于日历日期以外的因素的模型来预测蝴蝶的发育，从而使监测其种群的人员能够提高实地调查的有效性。在这项研究中，我们开发了两个简单的气候模型，一个使用年累积生长度日 (GDD)，另一个使用年累积短波辐射通量密度 (SRAD)，以确定这些变量是否可以预测三种蝴蝶的首次出现比基于站点首次观测的平均顺序日期的方法误差更大。此外，我们调查了结合我们的两个模型是否会增加我们预测首次出现时间的能力。我们确定，对于所有测试的物种，GDD 模型在预测首次出现方面优于序数日期模型和 SRAD 模型；然而，这些模型之间的实际差异非常小，以致于开发 GDD 模型所需的任何额外努力都不能证明此时将它们用作更简单的序数日期模型的替代品是合理的，尽管随着气候变化它们可能变得更加有用。我们还确定组合模型并没有提高预测首次出现的能力。