Acclimation and other forms of plasticity that can increase stress resistance feature strongly in discussions surrounding climate change impacts or vulnerability projections of insects and other ectotherms. There is interest in compiling databases for assessing the adequacy of acclimation for dealing with climate change. Here, we argue that the nature of acclimation is context dependent and therefore that estimates summarised across studies, especially those that have assayed stress using diverse methods, are limited in their utility when applied as a standardized metric or to a single general context such as average climate warming. Moreover, the dynamic nature of tolerances and acclimation drives important variation that is quickly obscured through many summary statistics or even in effect size analyses; retaining a strong focus on the temporal-level, population-level and treatment-level variance in forecasting climate change impacts on insects is essential. We summarise recent developments within the context of climate change and propose how future studies might validate the role of acclimation by integration across field studies and mechanistic modelling. Despite arguments to the contrary, to date no studies have convincingly demonstrated an important role for acclimation in recent climate change adaptation of insects. Paramount to these discussions is i) developing a strong conceptual framework for acclimation in the focal trait(s), ii) obtaining novel empirical data dissecting the fitness benefits and consequences of acclimation across diverse contexts and timescales, with iii) better coverage of under-represented geographic regions and taxa.

适应气候变化和其他形式的可增加抗逆性的可塑性在有关气候变化影响或昆虫和其他外温易感性预测的讨论中非常有特色。有兴趣汇编数据库以评估适应气候变化的适应性。在这里，我们认为适应的性质取决于上下文，因此跨研究总结的估计值，尤其是那些使用多种方法测定压力的估计值，在用作标准化指标或应用于单个一般上下文（例如平均值）时，其效用受到限制。气候变暖。此外，容差和适应的动态特性驱动了重要的变化，而这些变化很快被许多汇总统计信息甚至效果大小分析所掩盖。在预测气候变化对昆虫的影响时，必须重点关注时间水平，人口水平和治疗水平的差异。我们总结了气候变化背景下的最新发展，并提出了未来的研究如何通过跨领域研究和机制建模的整合来验证驯化的作用。尽管有相反的论点，但迄今为止，尚无任何研究令人信服地证明驯化在昆虫的最近气候变化适应中具有重要作用。这些讨论最重要的是：i）建立一个强大的概念适应性特质，ii）获得新颖的经验数据，剖析适应性益处和适应各种情况和时间范围的后果，iii）更好地覆盖以下方面：代表地理区域和分类单元。