Department of Zoology & Physiology and Program in Ecology, The University of Wyoming, Laramie, Wyoming 82071, USA.
Department of Biological Sciences, The University of Alabama, Box 870344, Tuscaloosa, Alabama 35487, USA.
Curr Opin Insect Sci. 2019 Dec;36:131-139. doi: 10.1016/j.cois.2019.09.003. Epub 2019 Sep 16.
Advances in tools to gather environmental, phenotypic, and molecular data have accelerated our ability to detect abiotic drivers of variation across the genome-to-phenome spectrum in model and non-model insects. However, differences in the spatial and temporal resolution of these data sets may create gaps in our understanding of linkages between environment, genotype, and phenotype that yield missed or misleading results about adaptive variation. In this review we highlight sources of variability that might impact studies of phenotypic and 'omic environmental adaptation, challenges to collecting data at relevant scales, and possible solutions that link intensive fine-scale reductionist studies of mechanisms to large-scale biogeographic patterns.
在收集环境、表型和分子数据的工具方面的进步加速了我们检测模型和非模型昆虫基因组到表型范围内非生物驱动因素变异的能力。然而，这些数据集在空间和时间分辨率上的差异可能会导致我们对环境、基因型和表型之间联系的理解存在差距，从而产生对适应性变异的遗漏或误导性结果。在这篇综述中，我们强调了可能影响表型和“组学”环境适应性研究的可变性来源、在相关尺度上收集数据的挑战，以及将机制的密集微观还原研究与大地理生物格局联系起来的可能解决方案。
