The adaptive value of transgenerational effects (the ancestor environmental effects on offspring) in changing environments has received much attention in recent years, but the related empirical evidence remains equivocal. Here, we conducted a meta‐analysis summarising 139 experimental studies in plants and animals with 1170 effect sizes to investigate the generality of transgenerational effects across taxa, traits, and environmental contexts. It was found that transgenerational effects generally enhanced offspring performance in response to both stressful and benign conditions. The strongest effects are in annual plants and invertebrates, whereas vertebrates appear to benefit mostly under benign conditions, and perennial plants show hardly any transgenerational responses at all. These differences among taxonomic/life‐history groups possibly reflect that vertebrates can avoid stressful conditions through their mobility, and longer‐lived plants have alternative strategies. In addition to environmental contexts and taxonomic/life‐history groups, transgenerational effects also varied among traits and developmental stages of ancestors and offspring, but the effects were similarly strong across three generations of offspring. By way of a more comprehensive data set and a different effect size, our results differ from those of a recent meta‐analysis, suggesting that transgenerational effects are widespread, strong and persistent and can substantially impact the responses of plants and animals to changing environments.

The comment by S anchez-T ojar et al. (2020, Ecol Lett) questioned the methodology, transparency and conclusion of our study (Ecol Lett, 22, 2019, 1976). The comment has overlooked important evolutionary assumptions in their reanalysis, and the issues raised were in fact dealt with through the peer-review process. Far from being biased, the key conclusion of our meta-analysis still stands; transgenerational effects are largely adaptive.

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.

Potato is the fourth staple crop in China after wheat, maize and rice. The agro-pastoral ecotone (APE) in North China is a main region for potato production. However, potato yield has been seriously constrained by water shortages because of low precipitation and highly variable precipitation patterns during the growing season in this area. In this study, the Agricultural Production Systems Simulator (APSIM) model was used to simulate potato water-limited yield and historical years were divided into different water-temperature year types to optimize the optimal planting period (OPP) and cultivar of potato. The results showed that the potato yield varied in different water-temperature year types. Fast-developing cultivar Favorita could obtain the highest yield in most places and water-temperature year types due to its relatively short length of tuber formation stage. In this study, we suggest changing the planting date according to the water-temperature year type, which offers a new way to adapt to a highly variable climate. However, our method should be adopted carefully because we only considered climate factors; other agronomic management practices (adjusting planting density, plastic film mulch, conservation tillage etc.) also have a great effect on planting date and cultivar selection, which should be further investigated in the future.

The comment by Sánchez-Tójar et al. (2020, Ecol Lett) questioned the methodology, transparency, and conclusion of our study (Yin et al. 2019, Ecol Lett, 22, 1976). The comment has overlooked important evolutionary assumptions in their reanalysis, and the issues raised were in fact dealt with through the peer-review process. Far from being biased, the key conclusion of our meta-analysis still stands; transgenerational effects are largely adaptive.